Adaptive fuzzy sliding-mode consensus control of nonlinear under-actuated agents in a near-optimal reinforcement learning framework |
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| Institution: | 1. Universidade Tecnológica Federal do Paraná, UTFPR, Av. Alberto Carazzai 1640, Cornelio Procópio 86300-000, PR, Brazil;2. Universitat Politècnica de Catalunya Barcelona Tech, Escola d’Enginyeria de Barcelona Est, CoDAlab (Control, Dynamics and Applications), Carrer d’Eduard Maristany, 10-14, Barcelona 08930, Spain;1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China;2. MetaX Technology Inc, Shanghai, 201210, China;3. Texas A & M University at Qatar, Doha, PO Box 23874, Qatar;1. School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China;2. Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China;1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China;2. Key Lab of Intelligent Data Information Processing and Control of Hebei Province, Key Lab of Intelligent Motion Control System of Tangshan City, Tangshan University, Tangshan, China;3. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China |
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| Abstract: | This study presents a new framework for merging the Adaptive Fuzzy Sliding-Mode Control (AFSMC) with an off-policy Reinforcement Learning (RL) algorithm to control nonlinear under-actuated agents. In particular, a near-optimal leader-follower consensus is considered, and a new method is proposed using the framework of graphical games. In the proposed technique, the sliding variables’ coefficients are considered adaptively tuned policies to achieve an optimal compromise between the satisfactory tracking performance and the allowable control efforts. Contrary to the conventional off-policy RL algorithms for consensus control of multi-agent systems, the proposed method does not require partial knowledge of the system dynamics to initialize the RL process. Furthermore, an actor-critic fuzzy methodology is employed to approximate optimal policies using the measured input/output data. Therefore, using the tuned sliding vector, the control input for each agent is generated which includes a fuzzy term, a robust term, and a saturation compensating term. In particular, the fuzzy system approximates a nonlinear function, and the robust part of the input compensates for any possible mismatches. Furthermore, the saturation compensating gain prevents instability due to any possible actuator saturation. Based on the local sliding variables, the fuzzy singletons, the bounds of the approximation errors, and the compensating gains are adaptively tuned. Closed-loop asymptotic stability is proved using the second Lyapunov theorem and Barbalat's lemma. The method's efficacy is verified by consensus control of multiple REMUS AUVs in the vertical plane. |
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