A NEURAL NETWORK CONTROL BASED OBSERVER FOR ROBOT MANIPULATORS

摘要

This article proposes a neural network controller using only joint position measurements for rigid robot manipulators. The joint velocity needed for the control law is estimated using an observer based on sliding mode technique. A decomposed structure neural network approximates the unknown model of the system. Each neural network approximates a separate element of the dynamical model. These approximations are used to perform an adaptive stable control law. The adopted neural network is of the MLP (Multi-Layer Perceptron) type, with one hidden layer. The Taylor-Young series is used to solve the nonlinearity problem of the MLP and to lead to the parameters adaptation algorithm. The corresponding parameters are the weights of the neural net. They are updated via the adaptation algorithm derived from stability study of the system in closed loop using Lyapunov approach and intrinsic properties of robot manipulators. Simulations have been conducted on a 2-DOF-robot manipulator to show the performances of the proposed controller.