Improved Dynamic Behavior in BLDC Drives Using Model Predictive Speed and Current Control

Abstract

Model-based predictive control is a powerful control strategy to drive electrical machines. Conventional cascade proportional-integral (derivative) [PI(D)] controllers are often used to control speed, torque, and current. However, for low-inertia machines, achieving a high-performance and wide speed range is far beyond the application conditions for which these controllers are designed for. Using two cascaded PI(D) controllers for the speed/current control of a low-inertia machine, changes in the speed set point should be slowly applied in order to avoid stability problems. In this paper, a model predictive control algorithm that is able to control the speed of a low-inertia brushless direct-current machine with a high bandwidth and good disturbance rejection properties is proposed. The algorithm is implemented on a SPARTAN 3E 1600 field-programmable gate-array board, and experimental results verify the performance of the proposed algorithm.