Integral Model Predictive Current Control for Synchronous Motor Drives

Abstract

Model predictive control (MPC) represents an affirmed optimal control strategy, able to handle multivariable systems and their input-output constraints. However, MPC does not provide an integral control action for reference tracking control problems. Several methods have been proposed to overcome this limitation. Standard MPC methods include a disturbance observer to handle unmodeled uncertainties, such as external unknown disturbances and parameter mismatches. Among these formulations, the authors focus on the velocity form MPC, which considers the incremental formulation of the motor state-space model. This formulation gets rid of the bias errors in reference tracking problems. In this article, the MPC paradigm is applied to the current control of synchronous motor drives. The intent is to compare the velocity form and the MPC with disturbance observer. A theoretical analysis of the MPC coupled with disturbance observers and the equivalence between these formulations and the velocity form is presented. Input constraints are included in the MPC optimization process, thus requiring an online quadratic programming solver. Experimental tests consider a 1 kW anisotropic synchronous motor. Numerical aspects regarding the optimization problem are investigated for both methods.