Dynamic modeling and control of pneumatic artificial muscles via Deep Lagrangian Networks and Reinforcement Learning

Abstract

Pneumatic artificial muscles (PAMs), as typical soft actuators characterized by hysteresis and nonlinearity, pose a challenging task in modeling and control. This paper proposes a Deep Lagrangian Networks Reinforcement Learning (DeLaNRL) controller that combines deep Lagrangian networks (DeLaN) with reinforcement learning to achieve precise motion control of PAMs. By leveraging the DeLaN model, the dynamic model is constrained to adhere to the Lagrangian first principle, enhancing the model’s compliance with physical constraints. Furthermore, to improve the generality and adaptability of the model to various input data, the Self-scalable tanh (Stan) function is employed as the activation function within the DeLaN model. To validate the effectiveness of the proposed modeling approach, the model is tested on both sampled and unknown motions. The results demonstrate the effectiveness and generalization capability of the DeLaN model with the Stan activation function. Subsequently, the reinforcement learning controller is applied to the learned dynamics model, resulting in control strategies capable of precise motion control. To further demonstrate the effectiveness of the proposed controller, experiments are conducted on both simulation and the experiment platform for reaching and tracking tasks. The simulation results indicate that the control error is less than 0.91 millimeters, while on the experimental platform, the control error is less than 3.7 millimeters. These results confirm that the proposed DeLaNRL controller exhibits high control performance.