SELF-TUNING PREDICTIVE CONTROL OF NONLINEAR SERVO-MOTOR

Vladimír Bobál – Petr Chalupa – Marek Kubalčík – Petr Dostál

   The paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo – motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and 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 and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model --- Amira DR300.

Keywords: non-linear system, servo-system, CARIMA model, self-tuning control, predictive control, real-time control

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