A Stable adaptive Neural-Observer by sliding mode for Rigid Robot Manipulators
نویسنده
چکیده
In this paper we present an adaptive appoach to observe the joint velocity for rigid robot manipulators. This approach is based on the neural network and sliding mode technique. The adopted neural network is of the MLP (Multi Layer Perceptron) type with one hidden layer. The functions of the dynamic model of the robot manipulator are assumed unknown. The neural network parameters are adapted on line in the goal to enssure the convergence of observation error. For stability we use the Lyapunov approach. Simulations have been conducted to show the performances of the proposed observer.
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