Adaptive observer based on MLPNN and sliding mode for wearable robots: Application to an active joint orthosis

This paper deals with the design of an adaptive observer based both on a Multi-Layer Perceptron Neural Network (MLPNN) and a sliding mode technique. Its main objective is to construct the complete state of a given exoskeleton worn by a human subject. The observer we propose in this paper can be used for any application: rehabilitation, assistance, etc. The dynamic model of the global system composed of the exoskeleton and the human is complex and supposed completely unknown. The MLPNN chosen for its characteristic of universal approximation has been used here to identify the unknown dynamic. Its parameters have been adjusted by taking into account the structure of the dynamic model of the considered system and the closed-loop stability based on Lyapunov׳s approach. A Taylor series expansion allows resolving the non-linearity problem present in the MLPNN. Besides the fact that the proposed adaptive observer can be integrated in a control scheme, it also allows us to study the behavior of the exoskeleton before any application on the human subject. The proposed study has been validated both in simulation and in experimentation. The obtained results show the effectiveness of the proposed adaptive approach.