Optimal Speed Controller Design of Commercial BLDC Motor by Adaptive Tabu Search Algorithm

Abstract

Brushless direct current motors are widely used in many industries because of their high efficiency and long-life. This paper presents the application of the adaptive Tabu Search algorithm for system identification and PID speed controller design to provide the best speed output performance compared with those designed using the well-known tuning method, the Ziegler-Nichols approach. The proposed design technique shows that the adaptive Tabu Search algorithm includes the control signal consideration in the design process. As a result, the resulting controller parameters can be implemented without the limitations of the real devices, while the Ziegler-Nichols method cannot provide as good a response as expected. The results are validated by simulation and experiment in which the output responses from the proposed design method are evidently better than those from the conventional method. Moreover, other artificial intelligence techniques such as the genetic algorithm, particle swarm optimization etc. can also be applied for the optimal design process following the concept in this paper in which the characteristics of the control signal are considered for real devices.