Keywords: Robot control

Keywords: Control applications, Fuzzy systems, Cooperative control
Abstract: This paper introduces a modified altitude hold system, designed for VTOL aircraft. The method differs from the conventional approach by blending pilot and controller commands, significantly reducing pilot workload. Authority blending ability is crucial during emergency and severe weather situations, where the feedback controller’s performance can prove insufficient. The developed control law was successfully verified with extensive pilot-in-the-loop simulations, utilizing the Tustin model of manual control. The resulting algorithm demonstrated smooth command blending throughout the flight envelope, enhancing safety and performance of VTOL aircraft operations in urban environment.

Keywords: Electric vehicles and intelligent transportation., Identification and estimation, Cyber-physical systems
Abstract: Network signal coordination control is a crucial means to improve the traffic operation efficiency of the overall roadway network. Accurate identification of critical paths does play an important role in determining the scope of network coordination control. Therefore, this paper proposed the definition of critical path from the perspective of traffic control and management. Under the detection environment of connected vehicle (CV), a comprehensive quantitative indicator system for path criticality evaluation from three aspects, supply side, demand side and operation side, which are arranged in the form of a tower structure. A critical path identification method (CPIM) was then proposed based on the analytic hierarchy process (AHP) theory, which was hereinafter referred to as AHP-CPIM. In order to evaluate the feasibility and effectiveness of the proposed method, a case study set in an urban network in Tongxiang, Zhejiang Province in China, is conducted through simulation models built through VISSIM and Synchro. Two scenarios were set, one is coordination control based on the coordination subarea obtained from Synchro (namely without critical path identification), and another one is coordination control with critical paths obtained from AHP-CPIM. Results showed that, compared with the control of Synchro and Multiband method under the scenario of coordination control without critical path identification, network signal coordination control optimization based on AHP-CPIM improved about 37.9% and 35.9% in average delay, respectively, justifying the effectiveness of CV-driven critical path identification for network signal coordination control.

Keywords: Object recognition, Image/video analysis, Image-based modeling
Abstract: Traditional chicken disease diagnosis methods rely on manual observation and experiential judgment, which are inefficient and susceptible to subjective factors. This paper aims to construct a chicken disease diagnostic model with deep learning algorithm for enhancing the accuracy and efficiency. Firstly, this paper proposes a chicken disease diagnostic model based on YOLOv8 algorithm. An SE-attention mechanism is designed for YOLOv8 structure to improve the detection accuracy. The SE-Attention based YOLOv8 detection model can identify and classify diseases by analyzing the feces images of chickens. Experiments on chicken disease diagnosis are performed to validate the proposed model's feasibility and effectiveness. Ablation study is constructed to validate the advantages of the SE-attention mechanism.

Keywords: Planning, scheduling and coordination, Intelligent automation, multi-robot systems
Abstract: Robotics and automation is one of crucial trend in smart manufacturing to improve production efficiency. Automated guided vehicles (AGVs) are a type of mobile robot used for material handling and have become widely utilized to achieve transportation automation. The use of multiple AGVs introduces potential risks, such as traffic conflicts and safety risk. To meet high production demands, many shop floors are set up for large-scale manufacturing. Thus, reasonable and efficient AGV scheduling is crucial for real-world operations.

This paper proposes an efficient prioritized planning algorithm for large-scale multiple-AGV scheduling problem in manufacturing. The algorithm sequentially addresses two primary sub-problems: job assignment and conflict-free routing. The results of job assignment dictate the routes taken by the AGVs. In job assignment, jobs are allocated sequentially based on their pickup times. In conflict-free routing, AGV priorities are predefined, ensuring that higher priority AGVs maintain their movement while adjustments are made only to lower priority AGV plans when conflicts arise. Avoiding conflict is regarded as to insert a new time window into a series of time windows, ensuring safety separation time on both sides of new time window with other time windows. Simulation is conducted on two real shop floor layouts and demonstrates the effectiveness and high efficiency of proposed algorithm. Even in a large-scale layout with 500 jobs and 20 AGVs, the computation time is only around 21 seconds.

Keywords: Human centered systems, Intelligent systems, Human-computer interaction
Abstract: This research proposed a model called rational intelligence and studied its reasoning capability as compared to ChatGPT-4o in counterfactual reasoning tasks. Unlike traditional AI models that rely heavily on data-driven approaches, rational intelligence allows for reasoning over abstract principles and hypothetical scenarios, similar to human cognitive processes. The proposed Rational Intelligence Model (RIM) applies Large Language Models (LLMs) to enable human-like comprehension, knowledge application, and problem-solving capabilities. In complex counterfactual reasoning tasks with scenarios proven to be challenging to human adults, we demonstrate that RIM achieves a clearly higher accuracy rate (76%) compared to ChatGPT-4o (68%), showing its enhanced reasoning capabilities. Additionally, RIM incorporates a self-reflection mechanism to manage knowledge conflicts and gaps, which can further improve its performance and adaptability.

Keywords: Robust control, Adaptive control
Abstract: This paper proposes an adaptive finite-time preassigned performance control (PPC) approach to solve the attitude control problem of hypersonic vehicles (HSVs) suffering from lumped disturbances. A specific preassigned performance function guaranteeing finite-time stability and small overshoot is presented to constrain the attitude tracking errors. Moreover, a novel synchronous disturbance estimation is presented to build a disturbance observer (DO). Based on the devised preassigned performance function and the information from the DO, an adaptive finite-time preassigned performance controller is devised to solve the attitude control problem. Finally, numerical examples demonstrate the validity of the presented algorithm.

Keywords: Consensus algorithms, Multi-agent systems, Nonlinear systems
Abstract: This article studies the resilient leader-follower tracking problem for the frequency control of distributed energy sources(DESs), and focuses on scenarios with a high density of misbehaving agents. To tackle the problem, we propose a fully distributed resilient consensus protocol, which utilizes confidence weights to evaluate the level of trust among agents with a first-order filter and a softmax-type function. The protocol theoretically ensures that the system is uniformly ultimately bounded, even in the presence of high-density misbehaving agents (that is, for each follower, it can have more misbehaving neighbors compared to normal neighbors). We also demonstrate the fast practical convergence of the proposed protocol with a simulation for a frequency control problem of DESs. Our study is a significant step towards enhancing the reliability and stability of modern power systems.

Keywords: Multi-agent systems, Cooperative control, Planning, scheduling and coordination
Abstract: This paper addresses the complex challenge of ensuring simultaneous arrival of multiple agents at a dynamic target, a critical requirement for operations such as roundups and saturation attacks. Traditional guidance systems have faced significant hurdles due to inaccurate flight time estimates and difficulties adapting to high-speed maneuvers. To overcome these limitations, we introduce a novel framework that utilizes Proximal Policy Optimization (PPO) and expert rules. This approach leverages distributed computing to enable autonomous decision-making among agents, thereby simplifying the deep reinforcement learning model and reducing computational overhead, which enhances scalability and adaptability. Additionally, we incorporate an angular velocity reward into the reward function, improving the predictability and effectiveness of maneuvers, particularly for targets with high-speed and irregular trajectories. The proposed methods have been rigorously tested through numerous simulations and high-fidelity scenarios, confirming their robustness and superior performance over traditional and enhanced proportional guidance systems.

Keywords: Multi-agent systems, Distributed optimization and MPC, Consensus algorithms
Abstract: This paper delves into a class of distributed nonconvex optimization control (DNOC) problems within multi-agent systems (MASs), aiming to attain consensus among agents while minimizing the collective sum of local cost functions, referred to as the global cost function, through local information exchange. To accomplish this objective, we introduce a novel predefined-time distributed optimal (PTDO) control algorithm. We prove that the proposed PTDO control algorithm guides the system states towards convergence to the global optimal solution within a user-defined time frame, provided the global cost function satisfies the Polyak-{L}ojasiewicz condition, which is less stringent than the standard strong convexity condition. Finally, theoretical findings are substantiated through simulation studies.

Keywords: Multi-agent systems, Neural networks, Control applications
Abstract: 本文提出了一种新的反向轨迹预测 增强型意图识别算法 （RTPEIR） 利用历史数据预测早期轨迹 并提高意图识别的准确性。The RTPEIR algorithm 包含两个主要增强功能。首先，它 利用过去和现在的轨迹数据的组合 重建先前的代理状态，从而提供 更深入地了解代理商的战略发展。 其次，它整合了这些重构的轨迹 随着持续的策略识别流程，显著 提高预测准确性。模拟实验 在星际争霸多智能体挑战赛 （SMAC） 上进行 平台显示 RTPEIR 优于现有的远期 轨迹预测模型降低了 6.5%，现有的 LSTM 和 GRU 模型分别下降了 13.3% 和 14.4%，就 意图识别准确性。此外，当组合使用 借助深度强化学习算法，RTPEIR 表现出胜率的显著提高A

Keywords: Multi-agent systems, Neural networks, Control applications
Abstract: Improving the obstacle avoidance capability of Unmanned Aerial Vehicles (UAVs) is crucial for maintaining their operational safety. UAVs with autonomous driving ability is the development trend of future aircraft. This paper introduces a novel UAV obstacle avoidance approach utilizing the Artificial Potential Field-Dueling Deep Q-Network (APF-Dueling DQN) method. According to the dynamic model of UAVs, the three-dimensional dynamic equation of UAVs is established, and the motion space of UAVs is constructed by combining pitch and heading angles. In order to improve the obstacle avoidance performance of UAVs, an improved Deep Reinforcement Learning (DRL) algorithm is designed, and the algorithm is used to improve the reward potential function of DQN algorithm. Simulation studies demonstrate that the APF-Dueling DQN approach surpasses the traditional DQN in performance, exhibiting robustness against local minima and yielding efficient, smooth flight paths. This underscores the efficacy of the APF-Dueling DQN in addressing UAV path planning problem.

Keywords: Multi-agent systems, Planning, scheduling and coordination, Cooperative control
Abstract: This paper proposes a flexible job-shop scheduling problem optimization method, which focuses on providing solutions for industrial production. First, in terms of model construction, the method further considers the cost of automated guided vehicles and the priority of workpiece production based on previous methods. Then, this method solves the model by non-dominated sorting genetic algorithm with self-cross and delete-mutation. It reduces the production time and energy by an average of 6.4% and 19.4%, which are 5.4% and 15.1% with the priority. Finally, the simulation verifies that the method effectively reduces the production cost while realizing the adjustment of the automated guided vehicle number and the workpiece production sequence.

Keywords: Adaptive control
Abstract: Machine-learning (ML)–based mixed traffic flow models have been gaining popularity in constructing model predictive control (MPC) for improving traffic flow operation efficiency. However, ML-based mixed traffic flow models are usually nonlinear, making it difficult to establish the relationship between driving behavior and traffic flow operating states, prohibiting its application in actual transportation systems. This study proposes a ML-based MPC with an model linearization scheme, which utilizes cellular automata to analyze the impact of the micro-level inside the traffic flow on the macro-level. On this basis, an RNN model is constructed using the operating data of cellular automata to establish the temporal relationship between the vehicle's following and lane-changing behavior and the traffic flow's operating efficiency. Finally, the neural network model is linearized based on the koopman operator, and a linear model predictive controller is designed to reduce computational costs. Results show that the koopman linear predictor has a lower prediction error than the taylor expansion based predictor, and the ML-based MPC can significantly reduce traffic congestion and make traffic flow operate smoothly.

Keywords: Nonlinear systems, Mobile robotics
Abstract: In this research work, we propose an optimal control law based on a variational approach for a spherical robot system with internal actuation. This system is particularly significant because it is nonlinear and has nonholonomic constraints. Furthermore, full Lie group symmetry is not available and is broken due to advection parameters arising out of subgroup symmetry. We address two control objectives: a contact position stabilization problem with zero angular velocities and that with constant spin. The control objective is defined through a cost functional encompassing both stabilization error and control effort. Energy optimal control law is deduced from a control Hamiltonian function using a variational approach, and finally, optimal state and costate dynamics are calculated. The performance of the proposed optimal controller is evaluated through simulation analysis.

Keywords: Nonlinear systems, Nano-scale automation and assembly, Control applications
Abstract: A quantum control theory of pure state in non-ideal quantum systems is proposed in this paper. A non-ideal closed quantum systems means that the controlled quantum system is in two degenerate cases: one is at least two transition frequencies between different energy levels are the same, or/and another is at least two eigenstates of the internal Hamiltonian are not directly coupled. The implicit Lyapunov control method based on the average value of an imaginary mechanical quantity is used to design the control laws. The design procedure of the imaginary mechanical quantity is derived. The relationships among the implicit Lyapunov control methods based on the state distance, the state error and the average value of an imaginary mechanical quantity are analyzed. Finally, some numerical simulation experiments are studied.

Keywords: Nonlinear systems, Adaptive control
Abstract: This paper investigates region tracking problem for a nonlinear system subject to measurement noise. Its purpose is to make enough use of the prescribed boundaries to reduce the fluctuations of control inputs and energy consumption. A novel region tracking control scheme is proposed based on bistable stochastic resonance. To construct a bistable stochastic resonance model, the tracking error is treated the Brownian particle and a weak periodic signal is added to the model, while the error in the dynamic loop is considered as noise. If the potential parameters are properly selected, the optimal stochastic resonance of the tracking error occurs. Then, the tracking error will switch between the potential wells determined by the potential parameters. Finally, the new design is applied on an over-actuated underwater vehicle. The results verify the superiority of the proposed controller in region tracking performances, including the use of the prescribed boundaries and fluctuations of control inputs.

Keywords: Nonlinear systems, Control applications
Abstract: The paper addresses the problem of transforming single-output nonlinear state equations affine in disturbance into an extended observer form whose nonlinear injection part depends additionally on the derivatives of the output up to a finite order. Moreover, the considered form is affine in disturbance. Based on the earlier results a detailed algorithm is given and applied to the model of a synchronous machine connected to an infinite bus. For the system in the extended observer form a reduced-order Kalman-filter based observer can be designed such that its error dynamics is exponentially attractive and it is stable in the input-to-state sense with respect to the disturbance.

Keywords: Adaptive control, Control applications, Robust control
Abstract: In this paper, an adaptive super-twisting algorithm is introduced for controlling a Brushless DC motor (BLDC) drive system.The main focus of this study is the assumption that the bounds of uncertainties and perturbations are unknown. The proposed control strategy employs dynamically adapted control gains to ensure the controller’s finite-time stability. A key aspect of the adaptation algorithm is its ability to avoid the overestimation of the control gain values. The controller’s effectiveness is demonstrated through simulation and experiments on a test setup.

Keywords: Biomedical instrumentation and applications, Feature extraction, grouping and segmentation, Sensor networks
Abstract: 穿墙生理参数监测在对独居个人的非侵入性健康监测和灾后救援行动中具有重要的应用潜力。针对多径效应和杂波干扰等挑战，该文提出了一种基于超宽带信号时频频谱图的穿壁生理信号重建方法。该方法使用奇异值分解 （SVD） 和最小二乘法有效缓解多径效应并提高信噪比。它进一步采用集成经验模态分解 （EEMD） 和时频频谱图进行信号分解和重建，准确估计呼吸和心率。消融实验表明，该方法提高了监测精度、稳定性，提高了信号可靠性。

Keywords: Distributed estimation, Sensor networks
Abstract: This paper deals with the problem of distributed estimation with consensus by utilizing a two-dimensional (2-D) system approach. The consensus protocol is employed as the information fusion strategy, enabling local communication among neighboring nodes to drive their estimates towards consensus, and the distributed dissipative filter is designed on each node. It is shown that the estimation error dynamics is asymptotically stable and meets the prescribed dissipativity performance. Moreover, by incorporating consensus updates in an additional time dimension, the estimator parameters are determined through a constructed 2-D system approach, thereby reducing the design complexity. Simulations demonstrate the validity of the proposed algorithm.

Keywords: Nonlinear systems, Mobile robotics, Robot control
Abstract: In this paper, we present a risk-aware motion planning framework for resilient navigation in occupancy grid maps. Navigating in unknown environments involves complex interactions with the surroundings that must be effectively managed by the robot. Recently, these interactions are frequently expressed as risk constraints, where risk is defined as potential threats that could hinder the robot from accomplishing its objectives. However, when the risk constraint involves nonlinearities, common numerical solvers are severely hampered in finding a feasible solution and are prone to failure. Therefore, we present a novel risk-aware navigation strategy based on motion primitives and the Nonlinear Model Predictive Control (NMPC) method to address nonlinear risk constraints within discrete maps. We demonstrate the effectiveness of our approach through a practical application of a robust risk assessment method that takes into account both the state of the environment and the state of the robot. In addition to enhancing the decision-making capabilities of the robot, our framework offers a more resilient motion planning process that enables the robot to navigate risky scenarios where standard optimizers are likely to fail and lead to dangerous trajectories.

Keywords: Intelligent systems
Abstract: Nowadays, handwriting still plays an important role in our lives, even though we live in a progressively more digital era. Therefore, losing the ability to write, whether because of physical or psychological complications, can become a huge handicap. The system described herein aims, in a first stage, to enable handwriting training through haptic guidance. The implementation was performed in Visual Studio and with the use of OpenHaptics toolkit. The second stage consists in character classification, written using a haptic joystick. The pre eminent procedure for digit classification consists in the use of Histogram of Oriented Gradients coupled with a multiclass Support Vector Machine (HOG-SVM) whereas for letters the strategy involves using a Convolutional Neural Network (CNN).

Keywords: Process automation, Discrete event systems, Hybrid systems
Abstract: In this contribution a new discrete event control scheme is proposed to ensure a defined and a reproducible process in reactive sputtering plants. The control scheme is based on a novel hybrid model that describes the pressure behaviour and the electrical behaviour of such processes. A new automation system is developed to allow the experimental validation of the control scheme, which consists of two parts. An interlock control prevents prohibited process states and a sequence control ensures a desired sequence of process states. Experimental data are shown to demonstrate the applicability of the discrete event control scheme and of the automation system.

Keywords: Space and underwater robots
Abstract: This paper introduces a model-based approach for diagnosing actuator faults in an underwater remote operated vehicle (ROV). The method employs structural analysis techniques to perform diagnosability analysis and to generate residuals, aiming to detect actuator faults at an early stage and issue timely warnings. To achieve this objective, we construct the mathematical model of the underwater robot, which facilitates the development of the system's structural model. This approach yields parity equations that serve as residual generators. A key advantage of the proposed method is its ability to provide practical solutions for residual generation in nonlinear systems. The CUSUM algorithm is used to detect changes in residual signals. The underwater remote operated vehicle BlueROV2 serves as the robotic platform for experimentation.

Keywords: Cyber security in networked control systems, Cyber-physical systems
Abstract: This paper considers the design of false data injection attacks towards cyber-physical systems without knowing system parameters. The existing research usually assumes that the attacker knows the plant’s parameters. However, this is often not the real case since the plant’s parameters are sensitive information that is not transmitted and thereby cannot be eavesdropped by external attackers. We propose an inference-based CPS attack mechanism where an attacker simultaneously updates an inference system and designs the injection attacks. More specifically, we introduce the notion of system imitator to cybersecurity for the first time and propose a bilevel imitation-based cyber-attack mechanism. We show that the attack mechanism can be implemented in an online manner and the convergence is theoretically guaranteed. Besides, we present a new cyber-threat model in communication networks, called network congestion attack, to illustrate the applicability of our approach.

Keywords: Cyber security in networked control systems, Distributed estimation, Multi-agent systems
Abstract: This paper investigates a design of a distributed prescribed-time observer for a class of multi-agent systems under homologous sensor attacks. First, we design Luenberger-like observers based on attack consensus which can accurately obtain the real state of the system at prescribed time. Then, the sufficiency condition and construction algorithm for the design of the observer gain matrices are given. Compared with the existing results of distributed state estimation under homologous sensor attacks, the observer proposed in this paper does not need to use all data in the time window and the prescribed convergence time is independent of the initial value of the system. Simulation results validate the effectiveness of the designed observer.

Keywords: Cyber security in networked control systems, Distributed estimation, Sensor networks
Abstract: This paper investigates the properties of the distributed filter under scaled noise injection attacks. Initially, three performance indices, along with three auxiliary indices, are introduced to evaluate the performance of the distributed filter. By providing the specific assumptions on the scaled parameters, the proportional relations among three performance indices are revealed. The relations of the gain matrices under the nominal and actual parameter scenarios are also examined. Furthermore, the impact of the fusion step on these relations is elucidated. These results provide guidance for designing the nominal noise covariance and evaluating the performance of the distributed filter under the scaled noise injection attacks. The theoretical results are validated through simulations.

Keywords: Robust control, Nonlinear systems, Mobile robotics
Abstract: The Scout rover is a novel rover concept developed by the German Aerospace Center (DLR) for the exploration of extreme extraterrestrial environments. Its locomotion platform is designed to tackle rough and rugged terrain, rock and boulder traverses, craters and cave systems on foreign celestial bodies. The Scout features a modular design with a compliant rover backbone and elastic rimless wheels. The backbone consists of rigid segments which are coupled by elastic vertebrae in-between. Under unfavorable conditions, the backbone deflects vertically, individual segments rear up and the rover begins to prance. Unintended backbone deformation and rearing-up negatively affect rover locomotion capabilities.

This contribution presents a robust kinematic chassis controller for monitoring and controlling the backbone configuration. The backbone deformation is generically modeled upon an elementary backbone unit based on the nonlinear beam theory and a kinematic surrogate model. Lyapunov theory is used to synthesize a backbone controller upon the surrogate model for the elementary backbone unit. A kinematic chassis controller is established upon the elementary backbone controller in order to reduce unintended backbone deformations on chassis level. The overall controller is verified by means of test scenarios on different terrains on the Scout outdoor testbed. The controller significantly reduces backbone deformation on rough terrains.

Keywords: Control applications, Intelligent systems
Abstract: The development of autonomous vehicles necessitates designing and testing advanced vehicle controllers that replace human drivers. Before reaching end users, these controllers must undergo rigorous experimental tests to verify their applicability and consistency with simulation data. Given the high cost of full-sized test vehicles, scaled vehicles offer a cost-effective and efficient alternative. This study evaluates state-of-the-art vehicle controllers for evasive maneuvers using a 1:3.33 scaled vehicle. The scaled vehicle is designed, constructed, and equipped with advanced hardware, including a centimeter-accurate GPS for localization and a MicroAutoBox III for executing the controllers. Experiments conducted with this scaled vehicle are compared to simulations performed in IPG-CarMakertextsuperscript{textregistered}, demonstrating an accuracy range of 83% to 99.9% between the scaled vehicle and virtual reality simulations.

Keywords: Delay systems, Networked control systems, Robust control
Abstract: This paper explores the stability problems of discrete-time linear time-invariant (LTI) systems subject to random time delays. We first develop a general mean-square small-gain stability condition for feedback systems containing structured stochastic multiplicative uncertainties. Subsequently, we apply this mean-square small-gain condition to two significant classes of LTI random delay systems, where delays may occur at random instants or exhibit random delay lengths. Necessary and sufficient mean-square stability criteria are derived. These criteria, applicable to systems with either single or multiple sources of random delays, typically involve computing the spectral radius of a constant matrix. This computation allows us to determine whether a system's state variance matrix converges asymptotically despite the presence of random delays.

Keywords: Distributed optimization and MPC, Multi-agent systems, Cooperative control
Abstract: Bilevel optimization has attracted substantial attentions in recent years due to its wide applications in machine learning. However, most existing algorithms either primarily developed under centralized setting or suffer expensive inner-loop updates for hypergradient estimation. What’s worse, the projection operator in constrained scenario may demand prohibitively computational cost, which further necessitates efficient projection-free bilevel optimization algorithms over networks. To fill this gap, we propose a novel single-loop distributed Frank-Wolfe algorithm DBO-FW for constrained bilevel optimization problems by simultaneously leveraging a nested approximation technique and a gradient tracking mechanism to locally estimate the global hypergradient. Moreover, we provide the convergence guarantee for the proposed DBO-FW. Numerical results also validate the efficiency of our algorithm.

Keywords: Planning, scheduling and coordination, Factory modeling and simulation
Abstract: Job shop scheduling is an important problem in the operations of manufacturing systems and has great potential for improving electricity efficiency. This paper studies an energy-aware job shop scheduling problem that aims to minimize the total energy cost and total weighted tardiness by selecting an optimal job schedule, considering operation and other constraints. To efficiently model operation constraints and to consider different machine types, a new set of linear integer constraints is developed. The energy-aware job shop scheduling problem considering time-of-use (TOU) electricity prices is then formulated as a mixed-integer linear programming (MILP) problem and solved using the branch-and-cut method. Numerical results tested against three examples demonstrate the computational efficiency and solution quality of our method.

Keywords: Sensor networks, Nonlinear systems, Distributed estimation
Abstract: This paper investigates the problem of refining the sensor positions in a wireless sensor network utilizing the time difference of arrival (TDOA) observations from multiple disjoint sources. Since the TDOA observations correspond to the same sensor position uncertainties, it is expected to further improve the sensor position accuracy based on the localization of the sources. Rather than the traditional two-step weighted least-squares approach that provides an approximate solution to source localization and sensor refinement problem, we firstly uses an iterative constrained weighted least-squares method to arrive at a more accurate localization of the multiple sources, which can be used to construct a set of range measurements with respect to each sensor. Finally, an iterative sensor position refinement algorithm is developed to fuse this range information and the prior sensor location information. Numerical Monte Carlo simulations are designed to verify the performance of the proposed method. The results illustrate that the corresponding average mean squared errors of both the source localization and sensor refinement are considerably smaller that those using the previous method.

Keywords: Smart grid, Consensus algorithms, Control applications
Abstract: Microgrid is a typical cyber-physical system which integrates the power flow and the communication flow. Privacy preservation of sensitive information delivering over the communication network has received significant attention. In this paper, a privacy-preserving distributed secondary controller is proposed for DC microgrids. The privacy information is subjected to an output mask function to ensure its anonymity, thereby providing real-time security for private power information. Furthermore, by employing event-triggered mechanism to determine the updating of control signals, current sharing and voltage regulation are achieved simultaneously. In addition, the decreasing event-triggered threshold improves the system’s response speed and accuracy. Finally, the simulation and experimental cases demonstrate the effectiveness of the proposed controller.

Keywords: Image/video analysis, Feature extraction, grouping and segmentation, Scene analysis
Abstract: Semantic segmentation is critical for autonomous driving systems that rely on accurate scene understanding to ensure safety and reliability. However, domain shifts between training and testing datasets, as well as challenges in nighttime conditions, significantly hinder the performance of semantic segmentation models when deployed in real-world driving scenarios. This paper presents an approach for addressing these challenges in semantic segmentation by leveraging an Information Bottleneck (IB)-inspired spatial attention mechanism. By integrating IB principles into the spatial attention framework, the model could capture essential features while filtering out irrelevant information, thereby improving its generalization capability across diverse domains and enhancing performance in nighttime segmentation. Extensive experiments demonstrate that IB-inspired attention consistently enhances domain generation and nighttime performance, showing robust semantic segmentation for autonomous driving.

Keywords: Nonlinear systems, Identification and estimation, Control applications
Abstract: Space vector pulse width modulation (SVPWM) is widely used in control of permanent magnet synchronous motor (PMSM). However, the unexpected harmonic current generates high-frequency radial electromagnetic force, so as to lead to the stator surface vibrations and noise. In order to alleviate this problem, a radial electromagnetic force reduction strategy based on random PWM switching frequency is proposed in this paper. Firstly, the high-frequency harmonic current and high-frequency radial electromagnetic force generated by SVPWM are quantitatively analyzed. And then, by using Beta probability distribution algorithm to randomly change the PWM carrier period, the spectrum of high-frequency harmonic current can be effectively scattered, thereby reducing the peak value of harmonic electromagnetic force. Finally, co-simulation results of Simulink and Maxwell verifies the effectiveness of the proposed strategy.

Keywords: Feature extraction, grouping and segmentation
Abstract: In this study, we tackle the critical challenge of sea-land segmentation for enhancing ship detection in remote sensing imagery. By focusing the search area, we significantly reduce the false alarm rate in ship detection. To overcome the limitations of scarce training data and annotation complexity, we introduce a novel zero-shot sea-land segmentation framework. This framework is integrated with a robust image segmentation model and employs an automatic prompt point generation strategy to facilitate sea-land discrimination. Our method is structured around three core components: image preprocessing, image block classification, and prompt point generation. The preprocessing module generates an edge feature map using downsampling and edge detection techniques. The classification module segments land and sea areas through a rasterization and classification process. The prompt point generation module leverages edge point clustering to create prompts on the land image block background. These prompts are then utilized by the image segmentation model to achieve precise sea segmentation. Our extensive experiments on public land-sea segmentation datasets show that our approach is robust across various parameter settings and outperforms current state-of-the-art methods.

Keywords: Intelligent systems, Multi-agent systems, Neural networks
Abstract: In this paper, we investigate the use of multi-UAVs (Unmanned Aerial Vehicles) for defense penetration in an urban environment with obstacles. To ensure a high-quality scenario of the urban environment, a digital-twin-city simulation platform is proposed featuring a powerful physics engine and real-time interaction technology. Subsequently, an improved multi-agent soft actor-critic (MASAC) method with a target prediction network is introduced to enhance the effectiveness of the cooperative multi-UAV system in defense penetration missions. Finally, the performance of the proposed improved MASAC method is evaluated through comparative experiments.

Keywords: Localization, navigation and mapping, Cyber security in networked control systems, Electric vehicles and intelligent transportation.
Abstract: With the rapid evolution of autonomous vehicles (AVs), ensuring reliable navigation has become paramount, especially against threats like Global Navigation Satellite Systems (GNSS) spoofing. This paper presents an attention-based Autoencoder approach for real-time GNSS spoofing detection in AVs. The proposed method leverages data from multiple sensors, including IMU, GNSS, and LiDAR, fully utilizing the redundancy and correlations among them. By integrating a multi-head attention mechanism into the Autoencoder, the model can thoroughly capture and analyze the complex relationships within sensor data, enhancing its capability to promptly and accurately identify spoofing attacks. Field experiments demonstrate the effectiveness of the proposed method in achieving a high detection rate and short detection time, highlighting its potential for practical deployment in AV applications.

Keywords: Localization, navigation and mapping, Robot sensing and data fusion, Mobile robotics
Abstract: In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. However, single LiDAR odometry often faces hardware failures and degradation in practical scenarios, and the continuous-time measurement characteristic is constantly neglected by existing LiDAR odometry. This motivates us to develop a continuous-time Multi-LiDAR Odometry (MLO) method, namely CT-MLO, which can realize accurate and real-time state estimation using multi-LiDAR measurements through a continuous-time perspective. Due to the advantageous continuous-time formulation, each LiDAR point in a point stream can query the corresponding continuous-time trajectory within its time instants. Additionally, a decentralized multi-LiDAR synchronization scheme is devised to combine points from separate LiDARs into a single point cloud without the need for primary LiDAR assignment. With the detailed derivation of the analytic Jacobians for continuous-time LiDAR observation, the proposed method integrates synchronization, continuous-time estimation, and voxel map management within a Kalman filter framework, which can achieve real-time state estimation with only a few linear iterations. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world autonomous driving experiments. The results demonstrate that the proposed CT-MLO can achieve high-accuracy continuous-time state estimations in real-time and is demonstratively competitive compared to other State-of-the-Art (SOTA) methods.

Keywords: Localization, navigation and mapping, Vision for robots
Abstract: For indoor environments, prior point-based visual SLAM cannot be processed in real time under low texture and illumination. To address this issue, this work proposes PLP-SLAM (Point-Line-Plane-SLAM) with RGB-D camera. Firstly, point and line features are detected in RGB images. For line features, establish length suppression and near line merge strategy to improve the line extraction quality. Secondly, plane features are extracted based on agglomerative hierarchical clustering method in point cloud obtained by RGB-D camera. Point clouds are divided into several nodes, unlike prior methods spend a lot of time to estimate the normal vector for each individual point, this work assumes that points within each node sharing the same plane normal vector, which can significantly improve the computational efficiency. Thirdly, sparse maps including points, lines and planes are established, meanwhile the scenes are reconstructed by creating the dense maps to show plan features directly. Finally, the performance of proposed method is compared against the state-of-the-art SLAM on public datasets to evaluate the pose estimation. All modules are run in real-time on a CPU, experiments clarify that PLP-SLAM can significantly enhance the robustness of 6DoF pose of the camera and simultaneously creating more detailed maps of the environment.

Keywords: Medical Image Analysis, Image-based modeling, Feature extraction, grouping and segmentation

Keywords: Tracking and surveillance, Image/video analysis, Object recognition
Abstract: The tracking-by-detection paradigm typically involves two primary steps: object detection and data association. In recent years, researchers have begun to introduce appearance features into the association stage to reduce ID switches and improve trajectory reconstruction capability. Although this approach has made significant progress, it still encounters various challenges, such as the susceptibility of appearance features to environmental noise and the inappropriate combination of motion and appearance information. To address above problems, we use the advanced motion-based OC-SORT as the baseline for improvement in this work. Firstly, we integrate appearance features into the association stage and use an adaptive appearance updating strategy to ensure the robustness of these features. Secondly, we adaptively adjust the weighting of motion and appearance features during the association stage, according to the number of consecutive frames in which the trajectory fails to match any detections. This adaptive adjustment aids in recovering objects that were lost due to long-term occlusion. Finally, we implement a camera motion compensation module, which enhances the accuracy of capturing object motion information correcting image displacement in real time, thereby further optimizing tracking performance. We validate the improved algorithm using multiple public datasets, and the experimental results show that our method significantly improves the stability of tracking.

Keywords: Discrete event systems, Control applications
Abstract: Decentralized control of deterministic systems has historically received less attention compared to extensive studies on stochastic cases since the 1970s. The challenge in deterministic systems lies in the difficulty of deriving controllers using methods such as the maximum principle or exact square sum, which are effective for stochastic cases. This paper presents the inaugural comprehensive solution for decentralized control of deterministic systems with inclusion information. The rationality of the designed controllers is substantiated through an analysis of their asymptotic optimality properties.

Keywords: Cyber security in networked control systems, Feature extraction, grouping and segmentation, Data analytics.
Abstract: This paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. The algorithm utilizes interpretable greedy trees as the foundational model and extends its applicability to handle multi-classification problems, thereby enhancing interpretability and detection accuracy. This methodology furnishes a more dependable and secure framework for discerning attack categories within the domain of network security. The experiment results show that the proposed algorithm achieves better balance between interpretability and identification precision.

Keywords: Cyber security in networked control systems, Networked control systems
Abstract: This study explores linear encryption techniques to protect against information-based stealthy attacks on remote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

Keywords: Learning and Statistical methods, Cyber security in networked control systems, Neural networks
Abstract: This paper advances a strategic paradigm for sensor power scheduling within Cyber-Physical Systems (CPSs) under adversarial scenarios, leveraging a Markov Stackelberg game combined with a Signal-to-Interference-plus-Noise Ratio (SINR) framework. A novel approach, which introduces reinforcement learning for the adaptive optimization of power distribution, is proposed to ascertain energy efficient sensor transmission tactics in the presence of jamming. Empirical simulations are given to validate the superiority of the proposed algorithm, underscoring its effectiveness in diminishing estimation inaccuracies and alleviating the adversarial influence.

Keywords: Distributed optimization and MPC, Consensus algorithms, Multi-agent systems
Abstract: This paper proposes a zero-gradient-sum-based time-varying distributed prescribed-time optimization algorithm for single integrator dynamics. The algorithm consists of a prescribed-time sliding mode term for achieving zero-gradient-sum and a sliding mode-based controller for achieving consensus among agents' states with a prescribed-time limit. Notably, the algorithm is free of both initial condition constraints and local minimization. The criteria for achieving consensus and optimizing multi-agent systems are derived from optimization theory and Lyapunov stability theory. Finally, the excellent convergence performance of the algorithm is verified through a power-sharing case study.

Keywords: Intelligent systems, Data analytics., Instrumentation systems
Abstract: Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretation of itself with formal language. Experiments with real datasets show the proposed neural network can obtain highly accurate fault diagnosis results with good computation efficiency. Additionally, the embedded formal language of the neuron network can provide explanations about the decision process, thus achieve interpretable fault diagnosis.

Keywords: Adaptive control
Abstract: In this paper, the data-driven optimal control problem is studied for continuous-time linear nonzero-sum games. Two kinds of reinforcement learning algorithms, i.e., reinforcement learning algorithm with data-storage based least-square method and reinforcement learning algorithm with filter based least-square method, are presented to obtain the Nash equilibrium solution. The properties of the presented reinforcement learning algorithms are analyzed. Simulation results show the efficiency of the presented reinforcement learning algorithms.

Keywords: Cooperative control, Consensus algorithms, Multi-agent systems
Abstract: This paper is concerned with the cooperative distributed optimization problem of a specific type of double-integrator multi-agent systems affected by DoS attacks. Firstly, a structure for attack detection and topology recovery is introduced to eliminate the effects of DoS attacks. Then, to estimate the state of the original system, a set of second-order homogeneous filters is designed to generate necessary state estimates. Furthermore, based on new coordinate transformations, the backstepping method is used to reconstruct the controller. According to the designed scheme, the output of each agent is able to converge to the global optimal solution. Ultimately, the feasibility of the theory is verified through simulation.

Keywords: Nonlinear systems
Abstract: In this paper, a finite-time model predictive control (MPC) framework is proposed for continuous-time systems subject to constraints. The proposed finite-time MPC is extended from the methodology for discrete-time systems, where the control horizon is set equal to the system dimension, and only terminal cost is penalized in the optimization. Zero-Order-Holder (ZOH) is applied to ensure that no ripple exists between two consecutive sampling intervals, such that finite-time transient process and zero steady-state error can be guaranteed. Combined with feedback linearization, the proposed deadbeat MPC is applicable to nonlinear continuous-time systems.

Keywords: Robust control, Adaptive control, Nonlinear systems
Abstract: This paper introduces an adaptive robust control approach tailored for underactuated mechanical systems encountering matched and mismatched uncertainty, employing a constraint-following methodology. The control strategy unfolds in two phases: initially, a nominal control scheme is devised neglecting uncertainty and deviations in initial conditions from constraints. Subsequently, uncertainty is categorized into matched and mismatched components, ensuring that mismatched uncertainties remain unobservable. Leveraging the structural characteristics of the uncertainty bound, a novel segmented adaptive law is proposed and seamlessly integrated into the adaptive robust control framework. By employing the Lyapunov minimax approach, the method ensures uniform boundedness and uniform ultimate boundedness simultaneously, thereby ensuring approximate adherence to constraints for underactuated mechanical systems facing both matched and mismatched uncertainties alongside initial condition deviations.

Keywords: Search, rescue and field robotics, Planning, scheduling and coordination
Abstract: Aiming at the deep-sea submarine search and rescue problem, this paper proposes a universally applicable submarine position prediction model and search model. The position prediction of the submersible is realized by Kalman filtering and dynamics modeling. Meanwhile, Monte Carlo method is used to construct the initialized population of the motion trajectory and optimize the search path step by step by iterative genetic algorithm. In this paper, the optimal search path is taken as the objective, the main ship SAR trajectory is simulated, and the probability of the diver is fitted as a function of time and cumulative search results. For the multiple submersible search and rescue problem, this paper proposes two possible search and rescue schemes and compares the search and rescue time. Then we propose the optimal multi-objective dynamic search and rescue method. In addition, this paper adopts the G1-entropy weight-coefficient of variation combined assignment method to evaluate different search devices and creatively adopts the CRITIC method to secondary assign the weights obtained from different evaluation methods to select the optimal search device. Finally, sensitivity and robustness analysis are also carried out in this paper.

Keywords: Fuzzy systems, Complex systems
Abstract: This study focuses on the observer-based feedback controller design of two-dimensional(2-D) discrete large-scale polynomial fuzzy systems. Firstly, a two-dimensional large-scale fuzzy system is established, with the 2-D polynomial Roesser model as the local model, including unknown interconnection terms. The separation property is established under the 2-D large-scale fuzzy framework. By this way, the fuzzy controller and the fuzzy observer can be individually solved and the corresponding closed-loop 2-D polynomial fuzzy system is asymptotically stable. The developed design algorithms are convex SOS conditions, which can be directly solved by the SOSTOOLS. Finally, a numerical example is shown to demonstrated the effectiveness of the proposed approach.

Keywords: Robust control, Complex systems, Nonlinear systems
Abstract: This paper introduces a novel Sliding Mode-Based Observer tailored for a specific subset of nonlinear systems of order mathbit{n} featuring Lipschitz nonlinearities. The study establishes stability conditions that ensure convergence of the estimation error (s) in finite time until order n, thus, providing an accurate state (s) estimation without the necessity for disturbance matching conditions. Furthermore, the study presents an extension of the scope of application of the proposed method to tackle a unique scenario characterized by a time-varying and non-invertible function of the output dynamics of the system model. The effectiveness of the proposed observer is showcased through simulation examples.

Keywords: Control applications, Nonlinear systems, Adaptive control
Abstract: 考虑到现有控制方法的依赖性 在塔式起重机模型上，无模型的自适应滑动 提出了模式控制算法进行定位 塔式起重机的防摆动问题。建议的控制器 结合了终端滑模控制和全格式 动态线性化 （FFDL） 数据模型来对抗 模型不确定性带来的问题。A 跟踪 Differentiator （TD） 还用于进一步改进 系统的动态性能。理论分析 证明控制误差和 所提算法的有效性由 模拟

Keywords: Medical Image Analysis, Image/video analysis, Feature extraction, grouping and segmentation

Keywords: multi-robot systems, Mobile robotics, Localization, navigation and mapping
Abstract: Quadrotor swarm with advanced coordination capabilities has garnered widespread attention, yet efficient and reliable motion planning remains a challenge. This paper presents a hierarchical motion planning framework for quadrotor swarm autonomous navigation in unknown, cluttered scenes. Specifically, we take a step forward sampling of multiple neighbors in the lattice graph and generate a group of paths. The infeasible paths are excluded and the one with the minimum cost is chosen from the remaining. Taking it as a guiding path, a gradient-free trajectory optimization method based on model predictive path integral (MPPI) is developed to produce the execution trajectory. Compared to gradient-descent approaches, it is capable of dealing with non-continuous and non-convex constraints. Additionally, the proposed method is deployed on GPUs in parallel to enhance efficiency. Extensive simulations demonstrate the robustness and effectiveness of the proposed motion planning framework.

Keywords: Object recognition
Abstract: Ship classification in remote sensing images holds paramount significance for surveillance and defense applications. However, the intricate nature of ship classes poses a challenge in accurately discriminating among diverse ship types. This challenge is exacerbated by the long-tailed data distribution, where certain classes are underrepresented by insufficient samples. Furthermore, the limited visual information available from single-perspective remote sensing imaging complicates the extraction of discriminative features. To address these challenges, we propose the Text-Guided Feature Mining Network (TGFMN). This approach aims to exploit strong textual knowledge to construct discriminative image features. We employ a unified classifier for pre-training to classify features from both modalities, ensuring that the dual modal features are integrated into a cohesive feature space, thereby maximizing the efficacy of textual information. The text-guided strategy is employed to enhance attention regions within the shallow spatial dimensions of visual features, facilitating the extraction of highly discriminative visual features during the fine-tuning process. The experimental results demonstrate the effectiveness of our method, achieving state-of-the-art performance on two benchmark datasets for fine-grained ship classification in remote sensing: FGSC-23 and FGSCR-42.

Keywords: Perception systems, Robot sensing and data fusion, multi-robot systems
Abstract: Traditional object detection methods are limited by single-sensor constraints, high computational requirements, and poor real-time performance. Also, occlusion often occurs under the condition of restricted single-view. This work introduces a camera and LiDAR fusion-based detection method, which achieves excellent detection performance under limited computational resources. We also explores a fusion detection method deployed with multi-view, which can effectively solve the occlusion issue encountered by single view. The proposed method is valuable for single view as well as multi-view in various application scenario. Our fusion method significantly improves detection accuracy and reliability, and solves the problem of data discrepancy, interference between sensors and occlusion due to restricted view. Experiments show that our object detection method has exhibited high accuracy and relatively low computational time.

Keywords: Robot sensing and data fusion, Perception systems, Vision for robots
Abstract: 3D object localization and intention prediction have become crucial components in autonomous system applications,such as self-driving car. However, there still faces a lot of challenges, especially for complex and dynamic scenarios where a single modality information is insufficient to effectively andprecisely localize the position and analyze the intention ofobjects. An improved method based on multimodal information fusion has been proposed via leveraging the advantages of 2D image segmentation and 3D geometrical characteristics of LiDAR point cloud. Extensive comparative experiments have been conducted and the results demonstrate that the proposed method significantly enhances both localization and prediction accuracy, comparing with the method where 2D bounding box of object instead of segmentation information is used to be fused with point cloud.

Keywords: Search, rescue and field robotics, Mobile robotics, Intelligent systems
Abstract: Autonomous exploration has become a crucial technology for mobile robots, and numerous broadly applicable algorithms have emerged. However, few exploration methods effectively utilize the features of specified types of areas to enhance the efficiency of autonomous exploration in a complex environment. In this paper, we propose ACS-MM-Explore, an adaptive-circular-search-based exploration framework for large-scale urban road environments, focusing on extracting and utilizing the boundaries of roads to enhance exploration efficiency. Our approach integrates a multi-modal traversability analysis module to distinguish between road and non-traversable areas on a 2D costmap. A novel mechanism for generating exploration viewpoints is introduced, efficiently creating exploration viewpoints with a circular search process with an adaptive radius. An optimized viewpoint selection mechanism is included, taking into account the geographical and geometric information of each viewpoint. The framework extends the move base and TEB local planner as a viewpoint-based navigation module. A comprehensive evaluation is concluded in a simulation environment, demonstrating the framework's effectiveness and robustness.

Keywords: Tracking and surveillance, Image/video analysis, Feature extraction, grouping and segmentation
Abstract: With the increasing application of multi-object tracking in practical scenarios, the problem of tracking object loss due to occlusion is receiving more and more attention. However, multi-object tracking techniques still face significant challenges, and the accuracy and robustness of tracking need to be further improved. To address these problems, a multi-object tracking algorithm based on motion estimation and adaptive appearance matching is proposed, using BoT-SORT as a baseline. First, a method called ME-DIOU is proposed to compute the similarity of targets based on motion estimation. This method improves the accuracy of matching tracked targets in successive frames when there is a loss of appearance features. Secondly, an adaptive thresholding method for appearance feature matching is proposed to improve the stability of appearance models in different scenes. Experiments were conducted on public datasets and the results show that the improved algorithm has better HOTA, MOTA and IDF1 values compared to the BoT-SORT algorithm. Compared to similar algorithms, the improved algorithm has more stable performance in dealing with occlusion and other problems, reducing the risk of losing tracking targets after occlusion.

Keywords: Multi-agent systems, Mobile robotics, Robot control
Abstract: This paper applies the principles of fluid mechanics to develop a new method for motion planning in contented environments where multiple groups of agents want to reach their target while guaranteeing inter-agent collision avoidance. Assuming 𝑀 groups of agents exist in the same motion space, we propose a time-varying ideal fluid flow model to safely plan the desired coordination of each group in the presence of other groups that are considered singularity points in the fluid flow field. To ensure that each group reaches its target destination, we propose to define the desired trajectory of each group along the streamlines of the fluid field but continuously direct the streamlines toward the target destination. The proposed solution, is experimentally evaluated by using quadruped robots in an indoor robotic facility.