Reinforcement Learning-Based Control for Electrohydraulic Actuators: A Case Study on an Inverted Pendulum Testbench

Electrohydraulic actuators (EHAs) are critical in applications demanding compact designs, high power density, and precise motion control. However, their nonlinear dynamics and inherent uncertainties pose considerable control challenges. This study develops a reinforcement learning (RL)-based control framework, integrating an actor–critic algorithm [deep deterministic policy gradient (DDPG)] and a novel reward-shaping method to address these challenges without requiring prior expert knowledge. The proposed approach is validated on a heavy-duty EHA-driven inverted pendulum testbench, as a benchmark system for nonlinear and unstable dynamics. Experimental results demonstrate the RL controller’s effectiveness in achieving the dual-control goal: swing-up and balancing of the EHA-driven pendulum. Furthermore, a comparative analysis with the classical linear quadratic regulator (LQR) highlights the strengths and limitations between RL-based control and model-based control. This study serves as fundamental research, offering practical and theoretical contributions to the application of RL in advanced motion control for EHAs and other complex systems.