Development of Admittance Control Method with Parameter Self-optimization for Hydraulic Series Elastic Actuator

Series elastic actuators (SEAs) have emerged as promising devices to enhance the safety of human-robot interactions in the manufacturing industry. However, the control of a hydraulic SEA under disturbance remains an unexplored issue. To address this problem, an admittance control method with parameter self-optimization is developed in this study. The hydraulic SEA and its dynamic model are first developed, and then, an admittance controller that combines a passive disturbance observer (DOB) and a feedback compensator is developed based on load movement dynamics. The control law of the framework is made independent of the hydraulic dynamics by considering the uncertainty and tracking error as disturbances. This simplifies the controller computation, enhances system robustness, and facilitates practical application. Next, the control performance is further improved by optimizing the control parameters using an improved crowding-based dynamic population size differential evolution (crowding-based dynNP-DE) algorithm. Benchmark and optimization experiments are performed to verify the superiority of the modified algorithm and obtain the control parameters. Finally, the optimized parameters are applied to practical experiments to validate the improved performance of the proposed admittance control scheme. The results show that the proposed method effectively reduces the SEA stiffness tracking error, with respect to the external contact force.