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Your search keyword '"He, Shuping"' showing total 3 results
Start Over Author "He, Shuping" Topic adaptive optimal control
3 results on '"He, Shuping"'

2. Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics.

Author

Fang, Haiyang, Zhu, Guozheng, Stojanovic, Vladimir, Nie, Rong, He, Shuping, Luan, Xiaoli, and Liu, Fei

Subjects
MATHEMATICAL optimization, ADAPTIVE control systems, NONLINEAR equations, ALGORITHMS, DIFFERENTIAL inclusions
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. [ABSTRACT FROM AUTHOR]

Published
2021
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3. Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach.

Author

He, Shuping, Fang, Haiyang, Zhang, Maoguang, Liu, Fei, and Ding, Zhengtao

Subjects
*ADAPTIVE control systems, *NONLINEAR systems, *DIFFERENTIAL inclusions, *RICCATI equation, *ALGEBRAIC equations, *OPTIMAL control theory, *NONLINEAR control theory
Abstract

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2020
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