Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

One of the main reasons that prevent the use of model-based predictive control (MPC) in power electronics and motor drive is the MPC design. The processes related to these fields have fast dynamics, and most of the MPC design guides involve the control of slow dynamic processes, which allow high sampling times and, consequently, a large time for the control action calculation. Moreover, practically only the linear model-based predictive controller can have small computational cost, which causes difficulty for MPC application in nonlinear processes as electrical motors. Therefore, this paper presents a MPC design theory for first-order control models, as the brushless direct current (BLDC) motor drive, assuming direct speed control (without current inner loop). Thus, the MPC cost function tune is obtained according to prediction model parameters, the study of optimization gain curves and, optionally, an extended closed-loop root locus analysis. To deal with the motor nonlinearities, both multi-model approach and six-step modulation are employed. This paper also discusses current peak transients, salient pole BLDC motor modeling, integral action included in the MPC control action and solutions for Hall effect sensor nonlinear speed measurement. Simulation and experimental results confirm the proposed design theory as well as the other discussed issues.