Variable Neural Direct Adaptive Robust Control of Uncertain Systems

Abstract

Direct adaptive robust state and output feedback controllers are proposed for the output tracking control of a class of uncertain systems. The proposed controllers incorporate a variable structure radial basis function (RBF) network to approximate unknown system dynamics, where the RBF network can determine its structure online dynamically. Radial basis functions can be added or removed to ensure the desired tracking accuracy and to prevent the network redundancy simultaneously. The closed-loop systems driven by the direct adaptive robust controllers are characterized by the guaranteed transient and steady-state tracking performance. The performance of the proposed output feedback controller is illustrated with numerical simulations.