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.

串联弹性执行器（SEA）已经成为有前途的设备，可以提高制造业中人机交互的安全性。但是，在扰动下对液压SEA的控制仍然是一个尚未探索的问题。为了解决这个问题，本研究开发了一种具有参数自优化的导纳控制方法。首先开发了液压SEA及其动力学模型，然后根据负载运动动力学，开发了将无源扰动观测器（DOB）和反馈补偿器相结合的导纳控制器。通过将不确定性和跟踪误差视为干扰，使框架的控制律独立于水力动力学。这简化了控制器的计算，提高了系统的鲁棒性，并促进了实际应用。下一个，通过使用改进的基于拥挤的动态种群大小差异演化（基于拥挤的dynNP-DE）算法优化控制参数，可以进一步提高控制性能。进行基准测试和优化实验，以验证改进算法的优越性并获得控制参数。最后，将优化的参数应用于实际实验，以验证所提出的导纳控制方案的改进性能。结果表明，相对于外部接触力，该方法有效地降低了SEA刚度跟踪误差。进行基准测试和优化实验，以验证改进算法的优越性并获得控制参数。最后，将优化的参数应用于实际实验，以验证所提出的导纳控制方案的改进性能。结果表明，相对于外部接触力，该方法有效地降低了SEA刚度跟踪误差。进行基准测试和优化实验，以验证改进算法的优越性并获得控制参数。最后，将优化的参数应用于实际实验，以验证所提出的导纳控制方案的改进性能。结果表明，相对于外部接触力，该方法有效地降低了SEA刚度跟踪误差。