Physics-Embedded Neural Feedback Linearization for Hydraulic Robot Joint Force Control with EKF Priors
Abstract
While hydraulic robots excel in high load capacity and interference immunity, achieving precise force tracking is hindered by complex fluid dynamics, system nonlinearities, and time-varying disturbances. This paper introduces a Physics-Enhanced Neural Network (PENN) approach. This method employs an Extended Kalman Filter (EKF) as a teacher to guide the prediction of hydraulic force dynamics. By effectively leveraging both data and physical baselines, this method achieved significant performance improvements, with the MSE, RMSE, and MAE decreasing by 85.5%, 61.9%, and 65.3%, respectively, compared with the traditional EKF baseline. Consequently, we propose a Neural Input-Output Feedback Linearization (NFBL) Proportional-Integral (PI) controller to globally linearize the nonlinear dynamics and track the desired force. The online EKF-PENN identifies the autonomous response and control gain terms of the hydraulic affine nonlinear system, providing accurate control variables under designed operating conditions. The method compensates for the pressure drop during hydraulic cylinder piston movement via flow compensation, while feedforward techniques significantly enhance force control response. Experimental results verify the effectiveness of these proposed methods. The proposed method can significantly improve the locomotion performance of hydraulically actuated legged robots.