Wiener structure based adaptive control for dynamic processes with approximate monotonic nonlinearities

Abstract

The modeling of industrial processes requires to consider the complex features of systems, such as nonlinearities, dynamics and uncertainties, etc. In this paper, a simplified Wiener structure (SWS) is proposed for the modeling of dynamic processes with approximate monotonic nonlinearities. The nominal SWS not only considers the dynamic characteristics in processes, but also takes full advantages of the process nonlinear properties. Then, an adaptive control method for the SWS is proposed, in order to achieve exact output tracking of reference signals in the servo control mode. The recursive estimation is implemented before the adaptive control law is ready. In the recursive computation, the ideas of both the discrete Nussbaum gain and the dead-zone factor are introduced. The tracking theorem verifies the stability of adaptive control under the suitable assumption. Finally, two illustrative examples demonstrate the effectiveness of both the SWS and the adaptive control.