An online learning model residual method for force control of hydraulic driven joint

Force control forms the foundation for achieving dexterous motion in hydraulic robots. Tracking the desired force with high accuracy and responsiveness faces persistent challenges from the nonlinearities of the valve-controlled cylinder system, as well as model inaccuracies due to model-plant mismatch, time-varying disturbances, and unmodeled effects. An input–output feedback linearization-based proportional–integral(PI) controller is introduced to globally linearize the nonlinear dynamics and track the desired force. This Controller is augmented with an online learning algorithm that fits the residuals between the nominal model and the actual plant to compensate for model inaccuracies. Additionally, current dither is introduced in the current loop control. Current dither effectively reduces spool startup friction, enhances spool dynamic response, and minimizes pressure hysteresis, thereby improving overall force control responsiveness performance. We validate these methods through comparisons with state-of-the-art approaches in terms of sinusoidal and step signal responses using simulations and a single joint testbed. The results demonstrate a 57% of the rise time improvement in step responses. This work advances hydraulic force control by synergizing model-based control with data-driven online adaptation, offering a deployable solution for high-dynamic robotic applications.