Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network

Abstract

A model-based fault accommodation control scheme that uses a recurrent wavelet Elman neural network (RWENN) is proposed to achieve satisfactory control without performance degradation for biped robot locomotion with unknown uncertainties and faults. In the fault accommodation scheme, a computed torque control is the main control that is used to track the desired trajectory when there is no fault; and a compensation control is used to eliminate the unknown model uncertainties. The proposed RWENN has an input from a context layer with self-feedback and an output recurrent layer to the hidden layer, which increases the precision and convergence time of the network compared with a recurrent neural network, a recurrent fuzzy neural network, and a recurrent wavelet neural network, so that any dynamic change, such as a fault on the system, can be estimated properly. Thus, it enhances the capability of fault accommodation. The adaptive laws of the RWENN-based fault accommodation control are derived from the Lyapunov theorem; hence, the stability of the system can be guaranteed. Finally, a case study of biped robot control with multiple faults and uncertainties is analyzed, and the effectiveness of the proposed fault accommodation scheme is demonstrated by simulation results. Its superiority is also assessed by a numerical comparison with other neural-network-based control schemes.