Artificial Neural Network Control of a Flexible-Joint Manipulator Under Unstructured Dynamic Uncertainties

This paper proposes a position control strategy based on artificial neural networks (ANN) in the face of structured and unstructured dynamic uncertainties. The control structure consists of a feedforward multilayer perceptron (MLP) to approximate the manipulator´s inverse dynamics online, a feedback radial basis function (RBF) neural network to compensate for the residual errors, and a reference model that defines the desired error dynamics. The online adaptation of the RBF neural network is is accomplished through two methods: (i) the least mean squares (LMS), and (ii) the recursive least squares (RLS) algorithms. A comparison study is conducted to evaluate the efficiency of both algorithms on the tracking ability of the proposed control scheme. Simulation results highlight the performance of the proposed control structures in compensating for the highly nonlinear unknown dynamics of the manipulator and its robustness in the presence of model imperfections.