In this paper,a homotopy-based reinforcement learning optimal control method is developed for Markov switched interconnected systems with unknown system dynamics.By utilising the subsystem decomposition method and par...In this paper,a homotopy-based reinforcement learning optimal control method is developed for Markov switched interconnected systems with unknown system dynamics.By utilising the subsystem decomposition method and parallel learning control method,the solution of the game coupled algebraic Riccati equations with jumping parameters is approximated.To dispense with the requirement of initial stability,a homotopy-based policy iteration is introduced,which can place unstable poles into a stable plane.In this regard,a model-free reinforcement learning method is presented to design the optimal controller for Markov switched interconnected systems.Finally,the effectiveness of the proposed method is verified by a numerical example and a practical example.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 62103005,62173001 and 62273006]The Natural Science Foundation for Distinguished Young Scholars of Higher Education Institutions of Anhui Province[grant number 2022AH020034]+2 种基金the Natural Science Foundation for Excellent Young Scholars of Higher Education Institutions of Anhui Province[grant numbers 2023AH030030 and 2022AH030049]the Research and development project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of Ministry of Education[grant number BWPU2023ZY02]the University Synergy Innovation Program of Anhui Province[grant number GXXT-2023-020]。
文摘In this paper,a homotopy-based reinforcement learning optimal control method is developed for Markov switched interconnected systems with unknown system dynamics.By utilising the subsystem decomposition method and parallel learning control method,the solution of the game coupled algebraic Riccati equations with jumping parameters is approximated.To dispense with the requirement of initial stability,a homotopy-based policy iteration is introduced,which can place unstable poles into a stable plane.In this regard,a model-free reinforcement learning method is presented to design the optimal controller for Markov switched interconnected systems.Finally,the effectiveness of the proposed method is verified by a numerical example and a practical example.
