Improving the Critic Learning for Event-Based Nonlinear H∞ Control Design
Document Type
Article
Date of Original Version
10-1-2017
Abstract
In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H∞ state feedback control design. First of all, the H∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.
Publication Title, e.g., Journal
IEEE Transactions on Cybernetics
Volume
47
Issue
10
Citation/Publisher Attribution
Wang, Ding, Haibo He, and Derong Liu. "Improving the Critic Learning for Event-Based Nonlinear H∞ Control Design." IEEE Transactions on Cybernetics 47, 10 (2017): 3417-3428. doi: 10.1109/TCYB.2017.2653800.