An Improved H∞ Tracking Controller for Uncertain Systems Based on DDPG with Improved Exploration Strategy

This paper proposes an integrated robust–learning control framework for uncertain systems with external disturbances. An H∞ state-feedback controller is first synthesized to ensure closed-loop stability and a prescribed disturbance attenuation level under norm-bounded uncertainties. Building on this robust baseline, the Deep Deterministic Policy Gradient (DDPG) is used to refine the H∞ feedback gains online to improve tracking and transient performance while reducing the conservatism of fixed robust gains. To improve the exploration process in reinforcement learning, a tracking-error-guided mechanism is developed. It adaptively adjusts the exploration intensity by means of tracking error energy, promoting reasonable exploration under steady-state conditions while suppressing excessive exploration during large transients, thereby improving both learning efficiency and system transient performance. The simulation results verify the effectiveness of the proposed method.