Adaptive Control of Unknown Dynamical Systems via Neural Network Approach

The feasibility of using an artificial neural network for controlling an unknown dynamical plant is investigated. A layered neural network is employed to learn the inverse dynamics of the unknown dynamical plant and acts as a feedforward controller to control the plant. This inverse dynamics is represented by the connection weights between the layers; these weights are adjusted based on the difference between the actual control input to the plant and the estimated input for achieving an actual plant output according to the inverse-dynamics model. The error back propagation scheme and the delta rule are used in the learning process. Simulation results are in this paper.