Online Barrier-Actor-Critic Learning for H∞ Control with Full-State Constraints and Input Saturation
This paper develops a novel adaptive optimal control design method with full-state constraints and input saturation in the presence of external disturbance. First, to consider the full-state constraints, a barrier function is developed for system transformation. Moreover, it is shown that, with the barrier-function-based system transformation, the stabilization of the transformed system is equivalent to the original constrained control problem. Second, the disturbance attenuation problem is formulated within the zero-sum differential games framework. To determine the optimal control and the worst-case disturbance, a novel barrier-actor-critic algorithm is presented for adaptive optimal learning while guaranteeing the full-state constraints and input saturation. It is proven that the closed-loop signals remain bounded during the online learning phase. Finally, simulation studies are conducted to demonstrate the effectiveness of the presented barrier-actor-critic learning algorithm.
Y. Yang et al., "Online Barrier-Actor-Critic Learning for H∞ Control with Full-State Constraints and Input Saturation," Journal of the Franklin Institute, Elsevier Ltd, Dec 2020.
The definitive version is available at https://doi.org/10.1016/j.jfranklin.2019.12.017
Electrical and Computer Engineering
Keywords and Phrases
E-Learning, Actor-Critic Algorithm; Actor-Critic Learning; Adaptive Optimal Control; Closed-Loop Signals; Constrained Controls; Disturbance Attenuation; External Disturbances; Zero-Sum Differential Games, Learning Algorithms
International Standard Serial Number (ISSN)
Article - Journal
© 2020 The Franklin Institute, All rights reserved.
01 Dec 2020