ADP-Based Intelligent Tracking Algorithm for Reentry Vehicles Subjected to Model and State Uncertainties

Abstract

This article presents an adaptive dynamic programming-based intelligent control algorithm for the attitude tracking issue of reentry vehicles subject to model and state uncertainties simultaneously. The traditional control approaches struggle to achieve satisfactory tracking performance since the model and state are together influenced and deviated by the both uncertainties. Instead, the attitude tracking issue in this article is first transformed into an optimal regulation issue of the tracking error. Then, a novel cost function inspired by the idea of zero-sum game is introduced to eliminate the model uncertainties, and state uncertainties are handled dynamically by updating weights based on the optimality principle of the critic network. Consequently, the intelligent tracking control law is obtained by the optimal regulation. The stability of the system and the convergence of network weights are further analyzed using the Lyapunov stability theory. The effectiveness of the proposed control scheme is verified by simulations.