Search

Your search keyword '"Zhang, Maoguang"' showing total 2 results
Start Over Author "Zhang, Maoguang" Topic algebraic equations
2 results on '"Zhang, Maoguang"'

1. Reinforcement learning and adaptive optimization of a class of Markov jump systems with completely unknown dynamic information.

Author

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

Subjects
*REINFORCEMENT learning, *ALGORITHMS, *RICCATI equation, *ALGEBRAIC equations, *ADAPTIVE control systems
Abstract

In this paper, an online adaptive optimal control problem of a class of continuous-time Markov jump linear systems (MJLSs) is investigated by using a parallel reinforcement learning (RL) algorithm with completely unknown dynamics. Before collecting and learning the subsystems information of states and inputs, the exploration noise is firstly added to describe the actual control input. Then, a novel parallel RL algorithm is used to parallelly compute the corresponding N coupled algebraic Riccati equations by online learning. By this algorithm, we will not need to know the dynamic information of the MJLSs. The convergence of the proposed algorithm is also proved. Finally, the effectiveness and applicability of this novel algorithm is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

2. 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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results