Identification and control of nonlinear systems using neural networks with variable structure control-based learning algorithms

This paper presents a Variable Structure Control (VSC)-based algorithm for adjusting a set of time varying parameters of virtual linear models that resemble linear dynamical neurons, used as on-line representations for a class of uncertain nonlinear processes. These virtual linear models allow the implementation of adaptive controllers in order to achieve predefined specifications for the closed-loop of the uncertain nonlinear process, or to force the tracking of the process output to reference models outputs accurately. A proof of the finite time convergence of the virtual linear model variables to the uncertain nonlinear process variables is included and some examples are contemplated to illustrate the proposed control design approaches.