Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics. (20th January 2021)

• Record Type: Journal Article
• Authors: Fang, Haiyang; Zhu, Guozheng; Stojanovic, Vladimir; Nie, Rong; He, Shuping; Luan, Xiaoli; Liu, Fei
• Other Names: Wan Yan guestEditor.; Yang Tao guestEditor.; Yuan Ye guestEditor.; Lewis Frank L. guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 6(2021)
• Page Start: 2126
• Page End: 2140
• Publication Date: 2021-01-20
• Subjects: adaptive optimal control -- linear differential inclusion -- Markov jump systems -- nonlinear -- policy iteration; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5350
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied. It is worth noting that the dynamic information of MJSs is partially unknown. Applying the neural network linear differential inclusion techniques, the nonlinear terms in MJSs are approximately converted to linear forms. By using subsystem transformation schemes, we can transfer the nonlinear MJSs to N new coupled linear subsystems. Then a new online policy iteration algorithm is put forward to obtain the adaptive optimal controller. Some theorems are given afterward to ensure the convergence of the new algorithm. At last, a simulation example is provided to verify the applicability of the algorithm.