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Multi-Objective Optimal Feedback Controls for under-actuated Dynamical system

Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 545-552页

作者： QIN Zhichang XIN Ying SUN Jianqiao School of Transportation and Vehicle Engineering Shandong University of TechnologyZibo 255000ShandongChina School of Mechanical Engineering Tianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory for Advanced Mechatronic system Design and Intelligent Control Tianjin University of TechnologyTianjin 300384China School of Engineering University of California MercedCA 95343USA

This paper presents a study of optimal control design for a single-inverted pendulum(SIP)system with the multi-objective particle swarm optimization(MOPSO)*** proportional derivative(PD)control algorithm is utilized to control the *** the SIP system is nonlinear and the output(the pendulum angle)cannot be directly controlled(it is under-actuated),the PD control gains are not tuned with classical *** this work,the MOPSO method is used to obtain the best PD *** use of multi-objective optimization algorithm allows the control design of the system without the need of linearization,which is not provided by using classical *** multi-objective optimal control design of the nonlinear system involves four design parameters(PD gains)and six objective functions(time-domain performance indices).The HausdorfF distances of consecutive Pareto sets,obtained in the MOPSO iterations,are computed to check the convergence of the MOPSO *** MOPSO algorithm finds the Pareto set and the Pareto front *** simulations and experiments of the rotary inverted pendulum system are done to verify this design *** and experimental results show that the multi-objective optimal controls offer a wide range of choices including the ones that have comparable performances to the linear quadratic regulator(LQR)control.

关键词： multi-objective optimal control under-actuated system particle swarm optimization(PSO) rotary inverted pendulum

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Trajectory Tracking Control for under-actuated Hovercraft Using Differential Flatness and Reinforcement Learning-Based Active Disturbance Rejection Control

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Journal of systems Science & Complexity 2022年第2期35卷 502-521页

作者： KONG Xiangyu XIA Yuanqing HU Rui LIN Min SUN Zhongqi DAI Li School of Automation Beijing Institute of TechnologyBeijing 100190China

This paper proposes a scheme of trajectory tracking control for the *** the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller *** solve this problem,the control scheme is divided into two ***,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as ***,we convert the under-actuated system into a full-actuated ***,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is *** this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control *** on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL *** results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.

关键词： Active disturbance rejection control differential flatness reinforcement learning trajectory tracking control under-actuated system

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