Model Reference Adaptive Control Using a Neural Compensator to Drive an Active Knee Joint Orthosis

International audienceThis paper presents an adaptive control approach of an actuated orthosis for the human knee joint rehabilitation. The objective of the proposed technique is to help patients to follow the guidelines of movement imposed by the therapists in terms of position and velocity. This is achieved by a system consisting of a mechanical orthosis actuated by an electrical driven motor. No needed prior knowledge concerning patients (height, weight, etc.). To prove the stability of the system, composed of the shank and the orthosis, in closed loop, we consider known its dynamic model structure. A Radial-Basis-Function Neural Network (RBFNN) is used to approximate online, a part of unknown dynamics and other unmodeled effects. In the goal to avoid abrupt transitions that can harm the wearer, we have used a reference model that can be constructed by an expert. The stability study conducted according to Lyapunov's approach guarantees that the proposed control remains stable even in the presence of bounded or assistive disturbances. The good performances of the proposed controller allow us to conclude with its effectiveness for trajectory tracking. In this work and for safety reasons, an adequate dummy has been used to perform real tests and detect any possible anomaly

Neural Network Identification For a C5 Parallel Robot

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

International audienceIn 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

A radial basis function neural network adaptive controller to drive a powered lower limb knee joint orthosis

International audienceThis paper deals with the rehabilitation purposes using an active orthosis driven by an adaptive neural controller based on a radial basis function neural network (RBFNN). Two essential conditions are required in our study: ensuring the wearer safety and the good trajectory tracking. We consider for our experiments the same movements often recommended by the doctor during therapy sessions. In this context, it is possible to add some trivial prior knowledge as the dynamic model structure and all dynamical identified parts. The unknown or the uncertainty part of the inertia term of the knee-shank-orthosis system is identified online using an adaptive term. All other uncertainties or unknown dynamics are identified online by the RBFNN. The Lyapunov approach has been used to derive adaptation laws of the neural parameters and the inertia term. These adaptation laws ensure the stability of the system composed of the exoskeleton and its wearer. The wearer can be completely inactive or applying either a resistive or an assistive effort. Experimental results have been conducted on a real exoskeleton that is used for rehabilitation reasons. Based on these results we conclude with the effectiveness of the proposed approach

A Stable Adaptive Force/Position Controller for a C5 Parallel Robot: A Neural Network Approach

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A Robust Adaptive Neural Controller to Drive a Knee Joint Actuated Orthosis

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