Parallel Control With Event-Based Adaptive Critic Implementation for Robust Optimal Tracking of Uncertain Nonlinear Systems

This paper investigates event-based robust optimal parallel tracking control for a class of uncertain nonlinear systems via adaptive dynamic programming (ADP). Analysis reveals that optimal control of the nominal system with sufficient feedback gain leads to robust optimal control. Then, optimal control is implemented online employing a critic neural network (NN), and event-triggered mechanisms (ETMs) are explored to update the weights intermittently. Among them, a unique dynamic event-triggered mechanism (DETM), which releases data in the light of an auxiliary term designed for stability verification, merits significant focus, and the comparison emphasizes its potential for better handling practical control challenges. Finally, experimental findings highlight the feasibility of the proposed robust control method while validating the characteristics and superiority of the ETMs.
Note to Practitioners
—As a significant issue in practical applications, event-based robust optimal control motivates this study. Distinguished from the previous efforts, this study develops the practical control difficulty into virtual space via parallel control and proportionally increases the nominal system’s optimal control rule for robust optimal control. The relaxation of both the system’s prior knowledge and the assumption related to the input dynamics allows the proposed method to be more compatible with practical systems. Moreover, ETMs are adopted to better balance control performance and resource occupation, with segmented DETM providing a fresh insight to effectively address practical control issues, i.e., releasing more data to prevent the system from being damaged by the disturbance while invoking less data to improve resource efficiency when the system is stable. Finally, the experimental comparisons validate the theoretical results.