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Optimal Tracking control for Leader-Following Consensus of Nonlinear Multiagent Systems

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3098-3107页

作者： Su, Wei Mu, Chaoxu Yang, Xiong Bian, Jinshan Sun, Changyin Tianjin University School of Control Science and Engineering Tianjin300072 China Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Hefei230601 China Anhui University School of Artificial Intelligence Hefei230000 China Southeast University School of Automation Nanjing210096 China

This article investigates distributed adaptive leader-following consensus tracking optimal control problem for nonlinear multiagent systems (MASs) subject to unknown nonlinearities and uncertain external disturbances. In contrast to traditional centralized control, the primary challenge is that only partial subsystems can access the desired reference trajectory. To address this, positive time-varying smooth function compensating terms are introduced to counteract the effects of uncertain external disturbances and unknown desired trajectories. Then, by fusing consensus errors into the backstepping technique, feedforward controllers are given. On this basis, the controlled nonlinear systems are transformed into an equivalent affine form, and feedback optimal controllers are designed using actor and critic neural networks (NNs) to execute control behavior and evaluate control performance. The whole control laws comprise both feedforward and feedback controllers. The proposed distributed adaptive consensus control protocol can simultaneously achieve desired optimal control performance and minimize the cost function, as demonstrated through theoretical analysis and simulation results. © 2025 IEEE.

关键词： Cost functions

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Reinforcement Learning Based Optimization of Autonomous Aircraft Performance 11

Reinforcement Learning Based Optimization of Autonomous Airc...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Sari, Metin Caliskan, Fikret Istanbul Technical University Department of Mechatronics Engineering Istanbul Turkey Turkish Aerospace Industries Ankara Turkey Istanbul Technical University Department of Control and Automation Istanbul Turkey

ISBN: (纸本)9798331509293

This research introduces an advanced flight control system for optimizing autonomous aircraft performance, leveraging deep reinforcement learning (DRL) to address the complexities of nonlinear flight dynamics. Using a six-degree-of-freedom (6-DoF) rigid aircraft flight dynamics model, we develop a Deep Deterministic Policy Gradient (DDPG) controller tailored for waypoint navigation and attitude stabilization tasks. A custom reward framework and extensive hyperparameter tuning enable effective training within a high-fidelity MATLAB/Simulink environment, achieving high rewards and precise control. Although computationally intensive, the simulations demonstrate robust performance across diverse flight conditions, with potential for real-world applications and future extensions to multi-agent scenarios. © 2025 IEEE.

关键词： Deep reinforcement learning

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Design and Performance Analysis of a Series-Parallel Self-Aligning Index Finger Exoskeleton

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IEEE Robotics and automation Letters 2025年第6期10卷 6456-6463页

作者： Sun, Ning Wang, Zelong Cheng, Long Li, Houcheng Niu, Ben Sun, Xijin Li, Ke Shandong Normal University School of Information Science and Engineering Jinan China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Shandong University Institute of Intelligent Medical Engineering School of Control Science and Engineering Jinan China

Hand exoskeletons have become increasingly crucial for the rehabilitation of hand function, as relevant studies have shown that using the exoskeletons to assist in rehabilitation training can improve hand motor function. However, developing a human-robot kinematic compatibility exoskeleton while providing sufficient assistance torque is still a challenge. This paper develops a series-parallel self-aligning index finger exoskeleton to assist the index finger in flexion/extension and adduction/abduction training. A series-parallel mechanism driven by two motors simultaneously was proposed for the metacarpophalangeal (MCP) joint, which can ensure human-robot joint axes self-alignment and increase the joint assistance torque. A kinematic compatibility analysis method was proposed, and the kinematic compatibility of the series-parallel mechanism was analyzed. The degrees of freedom and forward kinematics of the proposed mechanism were analyzed, and its workspace was calculated, which results show that the motion range of the MCP joint can meet the grasping training requirements. Preliminary experiments were carried out, and the results indicate that the motion trajectory coefficients of the marker points at the MCP, PIP, and DIP joints averaged for all subjects with and without the proposed exoskeleton are 0.993, 0.956, and 0.939, respectively, which can verify the kinematic transparency of the proposed mechanism. The assistance torque generated by the proposed mechanism on the MCP joint can meet the joint assistance torque requirements. In a word, the proposed exoskeleton can achieve flexion/extension and adduction/abduction training, human-robot joint axes self-alignment, and provide sufficient assistance torque. © 2016 IEEE.

关键词： Exoskeleton (Robotics)

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A Data-Driven Adaptive Optimal control with Input Saturation for Driverless Heavy-Duty Truck

A Data-Driven Adaptive Optimal Control with Input Saturation...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ji, Honghai Yang, Jianfeng Liu, Shida Fan, Lingling Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9789819639687

This paper mainly studies the longitudinal speed tracking problem of driverless heavy-duty trucks based on data-driven methods. A data-driven adaptive optimal control algorithm with control input saturation constraints is proposed in this paper to solve the problems of control input saturation and difficulty in establishing an accurate model in driverless heavy-duty truck systems. First, control constraints are added to the control input saturation problem of driverless heavy-duty trucks. Then, the dynamic linearization technology is used to model the nonlinear system of driverless heavy-duty trucks. After that, an appropriate index function is selected and the Newton-type optimization method is used to obtain the optimal controller. The method proposed in this paper only uses a small amount of input and output data and does not need to establish an accurate mathematical model. The simulation results show that compared with the PID algorithm, the proposed algorithm has advantages in speed tracking when applied to driverless heavy-duty truck systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Automobiles

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Energy-based Swing-up control of the Soft Inverted Pendulum with Affine Curvature

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Nonlinear Dynamics 2025年 1-20页

作者： Wang, Yongjia Xin, Xin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Institute of Intelligent Unmanned Systems Southeast University Nanjing210096 China

This paper studies the energy-based swing-up control of an underactuated soft inverted pendulum, a template model of soft robots with its base attached to the ground and its curvature described by an affine function. First, this paper presents a necessary and sufficient condition for the linear controllability of the robot around its upright equilibrium point (UEP). Next, without any constraint on its stiffness, this paper derives an energy-based controller to swing up the robot to its UEP, where the pendulum is in its upright position, and provides a necessary and sufficient condition such that the controller is free of singular points. Then, this paper characterizes the patterns of closed-loop equilibrium points with respect to the physical parameters of the pendulum and the gains of the derived controller, and analyzes the closed-loop motion corresponding to the special case in which desired convergence of the total mechanical energy is achieved. In such case, this paper reveals that the UEP is generically the unique closed-loop equilibrium point, and provides clarification regarding its stability. These results serve as guidelines of the design of soft robots and the tuning of control gains to achieve the desired performance of the swing-up control. Finally, this paper conducts numerical simulations to validate the theoretical results and to demonstrate the effectiveness and performance of the derived controller in swinging up the robot to its UEP from initial states far from the UEP, even under the challenging condition of very low stiffness. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Affine transforms

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Deterministic Learning-Based Tracking control for Parabolic PDE Systems With Infinite-Dimensional Nonlinear Uncertain Dynamics

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IEEE Transactions on Automatic control 2025年第5期70卷 3272-3287页

作者： Zhang, Jingting Yuan, Chengzhi Wu, Fen Wang, Cong Cheng, Hong University of Electronic Science and Technology of China Center for Robotics School of Automation Engineering Chengdu611731 China University of Rhode Island Mechanical Industrial and Systems Engineering Department KingstonRI02881 United States North Carolina State University Mechanical and Aerospace Engineering Department RaleighNC27695 United States Shandong University School of Control Science and Engineering Jinan250061 China

In this article, we investigate the joint problem of dynamics learning and tracking control for a class of parabolic partial differential equation (PDE) systems with infinite-dimensional uncertain nonlinear dynamics. A new learning control scheme is proposed based on the deterministic learning (DL) theory. One key feature of the proposed scheme is its capability of accurately learning the system's nonlinear uncertain dynamics during real-time tracking control with provable stability and convergence of the overall PDE closed-loop system. Specifically, the Galerkin method is first employed to deal with the infinite dimensionality of the PDE system;a novel DL-based adaptive learning control scheme is then proposed using dual radial basis function neural networks (RBF NNs), in which a pair of RBF NNs are employed to address, respectively, the matched and unmatched components of uncertain nonlinear system dynamics. This control scheme is finally examined on the original PDE system, and it is rigorously proved that: first the PDE system's state tracks the prescribed reference trajectory with guaranteed closed-loop stability and tracking accuracy;and second locally accurate identification of the PDE system's dominant nonlinear uncertain dynamics can be achieved with provable convergence of associated NN weights to their optimal values, thereby the learned knowledge can be ultimately stored and represented by the convergent constant RBF NN models. Based on this, an experience-based control scheme is further proposed, which is capable of recalling the associated learned knowledge in real-time to further improve control performance and reduce computational complexity with maintained provable stabilization. It is worth stressing that although this work is focused particularly on parabolic PDE systems, it is groundbreaking with important technical breakthroughs that would facilitate a more complete extension of the DL theory from traditional ordinary differential equation syste

关键词： Galerkin methods

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Vibration Suppression of AMBs-High Speed Motor Using an Enhanced Linear Extended State Observer

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IEEE Access 2025年 13卷 80822-80833页

作者： Gong, Lei He, Pai Dai, Dali Hua, Zhiguang Chen, Jingwen Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi’an710021 China Chongqing Dema Inverter Motor Research and Development Manufacturing Company Ltd. Chongqing400000 China Northwestern Polytechnical University School of Automation Xi’an710129 China

In order to solve the problem of rotor vibration caused by the unbalanced mass of the Active magnetic bearing (AMBs) rotor system, an Enhanced Linear Extended State Observer (ELESO) is designed based on the linear Extended State Observer (LESO). Firstly, the lumped disturbance including the unbalanced force is observed by ELESO, and the observed value is filtered by the band-pass filter (BF), and secondly, the lumped disturbance is compensated in the frequency band where the peak of rotor vibration is generated, so as to reduce the peak vibration of the rotor, and the saturation of the power amplifier caused by compensation in the full frequency band can be avoided. Then, the poles of the closed-loop AMBs rotor system are analyzed, and it is proved that the proposed control method can ensure the stable operation of the system. Finally, the correctness and effectiveness of the proposed method are verified under the working conditions of no disturbance and sensor runout. The results show that the designed control method has a smaller unbalanced vibration peak and a significant reduction in the control current when the rotor is operating in the full speed range compared with the direct use of LESO. © 2013 IEEE.

关键词： State estimation

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An Improved Lightweight Safety Helmet Detection Algorithm for YOLOv8

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Computers, Materials & Continua 2025年第5期83卷 2245-2265页

作者： Lieping Zhang Hao Ma Jiancheng Huang Cui Zhang Xiaolin Gao Key Laboratory of AdvancedManufacturing and Automation Technology EducationDepartment of Guangxi Zhuang Autonomous RegionGuilin University of TechnologyGuilin541004China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace TechnologyGuilin541004China Guangxi Tianli Construction Engineering Co. Ltd.Guilin541001China School of Information Engineering Nanning College of TechnologyGuilin541004China

Detecting individuals wearing safety helmets in complex environments faces several *** factors include limited detection accuracy and frequent missed or false ***,existing algorithms often have excessive parameter counts,complex network structures,and high computational *** challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded *** at this problem,this research proposes an optimized and lightweight solution called FGP-YOLOv8,an improved version of *** YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are *** modification minimizes computational costs with only a minor impact on accuracy.A new GSTA(GSConv-Triplet Attention)module is introduced to enhance feature fusion and reduce computational *** is achieved using attention weights generated from dimensional interactions within the feature ***,the ParNet-C2f module replaces the original C2f(CSP Bottleneck with 2 Convolutions)module,improving feature extraction for safety helmets of various shapes and *** CIoU(Complete-IoU)is replaced with the WIoU(Wise-IoU)to boost performance further,enhancing detection accuracy and generalization *** results validate the *** proposedmodel reduces the parameter count by 19.9% and the computational load by 18.5%.At the same time,mAP(mean average precision)increases by 2.3%,and precision improves by 1.2%.These results demonstrate the model’s robust performance in detecting safety helmets across diverse environments.

关键词： YOLO safety helmet detection complex environments lightweight WIoU loss function

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PI-Type Sliding Mode Fractional-Order Synchronization of a Memristive Chua Circuit

PI-Type Sliding Mode Fractional-Order Synchronization of a M...

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control, automation, and Instrumentation (IC2AI), International Conference on

作者： Dorukhan Astekin Fatih Adigüzel Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

In this study, a sliding mode fractional-order synchronization is proposed for the memristive chaotic Chua system. After the integer-order model of the chaotic memristive Chua system is given, a PI-type sliding surface-based controller structure combined with the fractional derivative operator is devised. The stability analysis of the synchronization closed-loop system is proven in the sense of Lyapunov theory. To demonstrate the effectiveness and applicability of the proposed controller structure, the fractional-order derivative operator in the controller structure is considered with two different fractional orders, and computer-based numerical simulation studies are performed comparatively.

关键词： Uncertainty Sensitivity System dynamics Simulation Main-secondary Stability analysis Robustness Synchronization Closed loop systems Integrated circuit modeling

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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