Neural network adaptive terminal sliding mode trajectory tracking control for mechanical leg systems with uncertainty

Abstract

This paper proposes an adaptive terminal sliding mode control based on neural block approximation for mechanical leg systems characterized by uncertainty and external disturbances. This control is based on a dynamic model of the mechanical leg and introduces an ideal system trajectory as a constraint. The structure of the paper is as follows. First, the RBF neural network is used to approximate the parameters of the dynamic model in blocks. This process is supplemented with a nonsingular terminal sliding mode surface to accelerate the convergence of tracking errors, and an adaptive law is used to adjust weights online to reconstruct the mechanical leg model. Next, an integral sliding mode control robust component is provided to mitigate external disturbances and correct model inaccuracies. Within this step, the Lyapunov method is used to prove the finite-time stability and uniform boundedness of the control system. Finally, the algorithm is validated and tested using the CAPACE rapid control system on a three-degree-of-freedom mechanical leg platform. The experimental results show that the proposed RBFTSM algorithm performs well in the performance evaluation of the MASE and RMSE values, with high trajectory tracking accuracy, anti-interference ability and strong robustness. Further evidence is presented to demonstrate the effectiveness and practicality of the proposed method.