Direct adaptive control for a class of MIMO nonlinear discrete-time systems

Abstract

This paper considers the problem of adaptive control for a class of multiple input multiple output (MIMO) nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor expand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adaptive neural network (NN) controller is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobally uniformly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. © 1990-2011 Beijing Institute of Aerospace Information.