Adaptive Super Twisting Sliding Mode Position Control for Series Elastic Actuator Robot using Radial Basis Function Neural Network

Series Elastic Actuators (SEA) provide improved control, safety, energy efficiency, and performance compared to traditional rigid actuators, making them well-suited for various applications in robotics, rehabilitation, and human-robot interaction. However, the inherent flexibility of SEAs can lead to oscillations in the position of SEA robots. This paper introduces a radial basis function (RBF) neural network-based adaptive super-twisting sliding mode control approach for position tracking of SEA robots. The robust control part of the proposed strategy, the super-twisting sliding mode control, effectively provides stability and robustness, demonstrates finite-time convergence, and suppresses oscillations caused by the joints. Beyond implementing robust control, the Radial Basis Function (RBF) neural network coordination is crucial for effectively approximating the unknown components of the manipulator dynamical model and uncertainties encountered in practical applications. The unknown nonlinearities are approximated through an RBF neural network, wherein the network's weight parameters are dynamically adjusted in real-time based on adaptive laws. Leveraging the RBF model, an adaptive control algorithm is formulated via the Lyapunov synthesis approach. Simulation outcomes corroborate the efficacy of the proposed controller in attaining accurate position tracking while effectively reducing oscillations. Mathematics Subject Classification (2020) 93C10 · 93C40 · 93B52 ·70Q05