Adaptive Dynamic Programming-Based H-Infinity–RLS Dual-Loop Cooperative Path Tracking Control

High-precision path tracking of Autonomous Mobile Robots (AMRs) is challenging due to nonlinear dynamics, external stochastic disturbances, and drastic payload variations. Traditional fixed-gain controllers often fail to maintain performance across varying load conditions, leading to safety risks and efficiency losses. This paper proposes a novel dual-loop adaptive control architecture combining H-infinity control based on Adaptive Dynamic Programming (ADP) and Recursive Least Squares (RLS) estimation. The inner loop utilizes an ADP framework with actor-critic neural networks to data-drivenly approximate the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, generating a robust optimal control law against worst-case external disturbances without requiring an exact system model. Simultaneously, the outer loop employs an RLS estimator to online identify time-varying parameters such as mass and friction coefficients, feeding these estimates back to the ADP controller to compensate for internal model mismatches in real-time. Simulation results conducted on a high-fidelity Gazebo/ROS2 platform under drastic payload variations (switching between 50 kg and 300 kg) demonstrate that the proposed method reduces tracking errors by 88% compared to classical PID and 82% compared to ADP-only baselines. Furthermore, it achieves exceptional load-invariant performance with a negligible error gap between empty and full loads, validating its effectiveness for high-reliability AMR operations in dynamic industrial settings.