Practical Implementation of Fast Adaptive Generalized Predictive Control for Systems with Variable Parameters

This paper proposes a fast adaptive predictive control structure which can be used to control systems with variable parameters. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates a recursive least squares identification method with forgetting factor to estimate online the parameters of a linear model used for prediction. In addition, the resulting optimization problem is solved using a method based on the Alternate Direction Method of Multipliers, which allows the control signal to be obtained with small computational effort. To validate the proposed algorithm an experimental case study which considers a direct current motor and a microcontroller STM32F303K8T6 is presented. Experimental results use as baseline the classical GPC algorithm and show that the proposed controller is able to keep almost the same transient response for all the considered operating points of the motor, while GPC presents high oscillations at operating conditions far from the one used to obtain the nominal model of the plant. Even though the proposed controller needs to solve two optimization problems at each sampling instant, it can run about 60 times in a second in the microcontroller used in this study.