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Stability analysis of linear/nonlinear switching active disturbance rejection control based MIMO continuous systems 被引量:2
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作者 WAN Hui QI Xiaohui LI Jie 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第4期956-970,共15页
In this paper,a linear/nonlinear switching active disturbance rejection control(SADRC)based decoupling control approach is proposed to deal with some difficult control problems in a class of multi-input multi-output(M... In this paper,a linear/nonlinear switching active disturbance rejection control(SADRC)based decoupling control approach is proposed to deal with some difficult control problems in a class of multi-input multi-output(MIMO)systems such as multi-variables,disturbances,and coupling,etc.Firstly,the structure and parameter tuning method of SADRC is introduced into this paper.Followed on this,virtual control variables are adopted into the MIMO systems,making the systems decoupled.Then the SADRC controller is designed for every subsystem.After this,a stability analyzed method via the Lyapunov function is proposed for the whole system.Finally,some simulations are presented to demonstrate the anti-disturbance and robustness of SADRC,and results show SADRC has a potential applications in engineering practice. 展开更多
关键词 linear/nonlinear switching active disturbance rejection control(SADRC) multi-input multi-output(MIMO)continuous system decoupling control stability analysis
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Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle 被引量:3
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作者 SONG Wanping CHEN Zengqiang +1 位作者 SUN Mingwei SUN Qinglin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第1期170-179,共10页
This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater ve... This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified. 展开更多
关键词 autonomous underwater vehicle(AUV) reinforcement learning(RL) Q-LEARNING linear active disturbance rejection control(LADRC) motion decoupling parameter optimization
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