Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton

A new approach to gait rehabilitation task of a 12 DOF lower limb exoskeleton is proposed combining time-delay estimation (TDE) based computed torque control (CTC) and robust adaptive RBF neural networks. In addition to the conventional advantages of the CTC, TDE technique is integrated to estimate unmodeled dynamics and external disturbance. To realize more accurate tracking, a robust adaptive RBF neural networks compensator is designed to approximate and compensate TDE error. The final asymptotic stability is guaranteed with Lyapunov criteria. To validate the proposed approach, co-simulation experiments are realized using SolidWorks, SimMechanics and MATLAB/Robotics Toolbox. Compared to CTC, sliding mode based CTC and TDE based CTC, the higher performances of the proposed controller are demonstrated by co-simulation. •A TDE based CTC method is proposed with a robust adaptive RBF neural networks.•To enhance CTC method, TDE is used to compensate unknown dynamics and disturbance.•To reduce TDE error, a robust adaptive RBF neural networks compensator is designed.•The asymptotic stability of exoskeleton control is guaranteed with Lyapunov theory.•Compared to CTC, SMC/TDE-CTC, high performances of the proposed method are validated.