Adaptive predictive control of nonlinear system with constraint of manipulated variable

Abstract

The paper is focused in design of an adaptive predictive control algorithm and its application for control of a laboratory servo - motor. The algorithm considers constraints of a manipulated variable. The adaptive predictive controller is based on an analytical design of a structure of the laboratory model and measured both static and dynamic characteristics. An ARX model is used in the identification part of the adaptive controller. Its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method. The optimization was realized by minimization of a quadratic objective function. The predictive controllers design is based on a CARIMA model of the second order. A recursive algorithm was designed for computation of predictions. This predictive controller was verified by a real-time control of highly nonlinear laboratory model - Amira DR300 Speed Control with Variable Load.