MLPNN adaptive controller based on a reference model to drive an actuated lower limb orthosis

In this paper we propose to drive an actuated orthosis using an adaptive controller based on a reference model. It is not necessary to know all the functions of the dynamic model. Needing only the global structure of the dynamic model, we use a specific adaptive controller to obtain good performance in terms of trajectory tracking both in position and in velocity. A Multi-Layer Perceptron Neural Network (MLPNN) is used to estimate dynamics related to inertia, gravitational and frictional forces along with other unmodeled dynamics. The Lyapunov formalism is used for stability study of the system (shank+orthosis) in closed loop and to determine adaptation laws of the neural parameters. To treat the non-linearties related to the MLPNN, we have used first order Taylor series expansion. Experimental results have been obtained using a real orthosis worn by an appropriate dummy. Several tests have been realized to verify the effectiveness and the robustness of the proposed controller. For instance, our proposed orthosis model has given robust tracking performance under assistive as well as resistive forces.