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Data-driven finite-time adaptive attitude tracking control for spacecraft

Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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Multi-agent Reinforcement Learning Algorithm Based on Role Parameter Sharing 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhang, Hui Xu, Chi Zhang, Ting-Ting Zhang, Ya School of Automation Southeast University Nanjing210096 China High-Tech Department Jiangsu Automation Research Institute Lianyungang222061 China School of Command and Control Engineering Army Engineering University Jiangsu Nanjing210017 China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

In multi-agent reinforcement learning, scalability is a significant challenge. Applying reinforcement learning algorithms in large-scale multi-agent systems requires consideration of algorithm efficiency. This paper attempts to reduce network parameter quantity and training time from the perspective of parameter sharing. However, parameter sharing may also bring some disadvantages, such as policy similarity, which may limit the formation of cooperative policies and affect the efficiency of the entire system. To address the problems caused by parameter sharing, this paper proposes a multi-agent reinforcement learning algorithm with role parameter sharing (R-PS). By dynamically dividing roles and selecting objects for parameter sharing, R-PS automatically identifies intelligent agents that may generate beneficial parameter sharing and conducts partial parameter sharing within the roles. Meanwhile, intrinsic rewards are introduced to increase the diversity of agent strategies. Experimental results show that R-PS can effectively divide roles, benefit from partial parameter sharing, and form diverse policies through intrinsic rewards. It also reduces training time, produces excellent performance and collaboration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Dynamic Event-Triggered Fixed-Time Consensus Analysis of Multi-Agent Systems Under Input Delays and Switching Topologies 3

Dynamic Event-Triggered Fixed-Time Consensus Analysis of Mul...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Zhang, Yu Li, Yajing Ran, Guangtao Yu, Hao Ma, Guangfu Department of Automation Shenzhen518055 China Shanghai Institute of Aerospace System Engineering Shanghai200241 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798331540319

This paper proposes a dynamic event-triggered control method for first-order multi-agent systems with input delays and disturbances under switching topologies. The controller design and its properties of dynamic event-triggered fixed-time consensus are presented in detail. Furthermore, agents can achieve consensus in a fixed time for any initial conditions and topologies that comply with the control protocol. Zeno behavior is avoided. Simulation examples demonstrate the effectiveness of the proposed algorithm and simulate scenarios in which the communication environment deteriorates due to external disturbances affecting the MASs. © 2024 IEEE.

关键词： Multi agent systems

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive control

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IEEE Transactions on Automatic control 2024年第6期70卷 3920-3935页

作者： Zheng, Kaikai Shi, Dawei Shi, Yang Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Victoria Department of Mechanical Engineering Faculty of Engineering VictoriaBCV8N 3P6 Canada

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a pre-specified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies. © 1963-2012 IEEE.

关键词： Predictive control systems

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Data-driven consensus control of fully distributed event-triggered multi-agent systems

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Science China(Information Sciences) 2023年第5期66卷 147-161页

作者： Yifei LI Xin WANG Jian SUN Gang WANG Jie CHEN School of Automation Beijing Institute of Technology The State Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Department of Control Science and Engineering Tongji University

This study investigates the consensus control issue in discrete-time linear multi-agent systems(MASs) using data-driven control under undirected communication networks. To alleviate the communication burden, an adaptive event-triggered control strategy involving only local information is proposed and a model-based stability condition is derived that guarantees the asymptotic consensus of MASs. Furthermore,a data-based consensus condition for unknown MASs is established by combining a data-based system representation with the model-based stability condition, using only pre-collected noisy input-state data instead of the accurate system information a priori. Specifically, both model-based and data-driven event-triggered controllers can be utilized without requiring any global information. The validity and correctness of the controllers and associated theoretical results are demonstrated via numerical simulations.

关键词： distributed control event-triggered control data-driven control discrete-time MASs

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Target Capturing in an Ellipsoidal Region for a Swarm of Double Integrator Agents

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 801-811页

作者： Antonio Bono Luigi D'Alfonso Giuseppe Fedele Veysel Gazi IEEE Department of Informatics ModelingElectronics and Systems EngineeringUniversity of CalabriaRende 87036Italy Dept.Electrical and Electronics Eng. Faculty of EngineeringMarmara UniversityIstanbul 34722 Department of Control and Automation Engineering Faculty of Electrical and Electronics EngineeringYildiz Technical UniversityEsenler 34220Turkey

In this paper we focus on the target capturing problem for a swarm of agents modelled as double integrators in any finite space *** agent knows the relative position of the target and has only an estimation of its velocity and *** that the estimation errors are bounded by some known values,it is possible to design a control law that ensures that agents enter a user-defined ellipsoidal ring around the moving *** know the relative position of the other members whose distance is smaller than a common detection ***,in the case of no uncertainty about target data and homogeneous agents,we show how the swarm can reach a static configuration around the moving *** simulations are reported to show the effectiveness of the proposed strategy.

关键词： Agents-based systems cooperative control swarms target-capturing

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Reward Drops in Learning-based control with an Experimental Validation on Microdrones 63

Reward Drops in Learning-based Control with an Experimental ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Tukenmez, Nejat Fotiadis, Filippos Junior, Jose M. Magalhaes Vamvoudakis, Kyriakos G. Bogosyan, Seta O. Istanbul Technical University Mechatronics Engineering Department Istanbul34469 Turkey Georgia Institute of Technology School of Aerospace Engineering AtlantaGA United States Istanbul Technical University Control and Automation Engineering Department Istanbul34469 Turkey

ISBN: (纸本)9798350316339

In this paper, we consider a computationally efficient learning-based control mechanism dealing with dense reward processing for a zero-sum game. The problem has been formulated as the online learning of the Nash equilibrium without requiring any information on the system dynamics. It has firstly been constructed as an infinite horizon optimal control problem, then as an online model-free Q-learning framework, which is composed of critic and actor networks (i.e., for the control and disturbance input). The closed-loop system is also proved to have a stable equilibrium point even in the presence of reward drops. The efficacy of the learning-based controller has been validated through simulations and experiments on micro drones. © 2024 IEEE.

关键词： Contrastive Learning

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A Real-Time Semantic Segmentation Method Based on Transformer for Autonomous Driving

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Computers, Materials & Continua 2024年第12期81卷 4419-4433页

作者： Weiyu Hao Jingyi Wang Huimin Lu Donald Bren School of Information and Computer Sciences University of CaliforniaIrvineCA 92612USA Department of Control Engineering Kyushu Institute of TechnologyKitakyushu804-8550Japan School of Automation Southeast UniversityNanjing210096China

While traditional Convolutional Neural Network(CNN)-based semantic segmentation methods have proven effective,they often encounter significant computational challenges due to the requirement for dense pixel-level predictions,which complicates real-time *** address this,we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving,utilizing the capabilities of Visual *** leveraging the self-attention mechanism inherent in Visual Transformers,our method enhances global contextual awareness,refining the representation of each pixel in relation to the overall *** enhancement is critical for quickly and accurately interpreting the complex elements within driving sce-narios—a fundamental need for autonomous *** experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods,achieving a significant 4.5%improvement in Mean Intersection over Union(mIoU)while maintaining real-time *** paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation *** work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks,thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making *** experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision,particularly in enhancing the interaction between automated systems and their dynamic environments.

关键词： Visual transformer semantic segmentation autonomous driving

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Uncertainty and its Effect on Optimal Multidrug control of Hemodynamic Variables

Uncertainty and its Effect on Optimal Multidrug Control of H...

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2024 American control Conference, ACC 2024

作者： Popescu, Teodora Birs, Isabela R. Othman, Ghada Ben Yumuk, Erhan Mihai, Marcian Hegedus, Erwin Copot, Dana De Keyser, Robin Ionescu, Clara M. Muresan, Cristina I. Technical University of Cluj-Napoca Department of Automation Cluj-Napoca Romania Ghent University Research Group on Dynamical Systems and Control Department of Electromechanics Systems and Metal Engineering Tech Lane Science Park 125 Gent9052 Belgium Istanbul Technical University Department of Control and Automation Engineering Maslak Istanbul34469 Turkey

ISBN: (纸本)9798350382655

Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptible to human errors. Optimal drug dosing is best achieved using automatic control, which triggers important benefits in terms of both costs and patient side-effects. However, mathematical models for patients are highly susceptible to large modeling uncertainty. A predictive control algorithm is designed in this paper for optimal multidrug control of hemodynamic variables. Improved closed loop performance is obtained compared to similar control strategies, for ± 30% modeling uncertainty. The simulation results demonstrate that predictive control is a feasible solution for optimal drug dosing. An analysis of the closed loop performance for significant patient variability shows that controllers tuned using a nominal patient model often fail to achieve desired robustness. To limit the effect of modeling uncertainty, the prediction model should be updated using an online identification tool to extract patient features. © 2024 AACC.

关键词： Predictive control systems

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