Experiments in adaptive control using artificial neural networks

Abstract

A method for using artificial neural networks (ANNs) to control nonlinear, multi-input/multi-output dynamical systems with unknown dynamics is investigated. A neuromorphic controller (NMC) and its application to a nonlinear self-tuning regulator problem is discussed. The NMC performs functions similar to those of adaptive controllers discussed in modern control theory, with the controller taking the form of a nonlinear network and the adaptable parameters being the synaptic interconnection strengths between neurons. The NMC is used to learn a model of the unknown system and to generate the control signals given both the measurements of the current states and the desired values of the current states. The model dynamics is represented by a set of tunable connection weights of the ANN whose weights are adjusted sequentially by a nonlinear recursive-least-squares (NRLS) algorithm which minimizes the error between the desired and current plant states. In effect, the NRLS algorithm trains the ANN to construct mappings of the current state of the plant to the control actions required to maintain the output of the plant at a prespecified value or along a desired trajectory