Robust Adaptive H∞$$ {H}_{\infty } $$ Tracking Control for Nonlinear Strict‐Feedback System via Actor‐Critic‐Disturbance Learning: A Zero‐Sum Game‐Based Approach

This article presents a robust adaptive tracking control scheme for a class of nonlinear strict‐feedback systems. Building upon robust control principles for disturbance attenuation and the nonlinear backstepping technique for stabilization, the proposed approach unifies robust tracking design with online adaptive dynamic programming for strict‐feedback nonlinear systems. Finite‐energy disturbances are explicitly modeled as an adversarial player in a zero‐sum game, thereby bridging the gap between robust tracking synthesis and data‐driven online optimization. To approximate the Nash equilibrium, a novel Actor‐Critic‐Disturbance learning algorithm is developed, where the sufficiency of excitation condition combined with the experience replay mechanism ensures adaptive weight convergence without relying on the restrictive persistence of excitation. Furthermore, the value function is decomposed into a quadratic part and a nonlinear component, thereby guaranteeing satisfactory tracking performance while avoiding direct solutions of the Hamilton–Jacobi–Isaacs equation. Theoretical analysis establishes rigorous justification for the stability of the overall closed‐loop system, and simulation studies validate the effectiveness of the proposed method.