Self-tuning PID control of a brushless DC motor by adaptive interaction

In this paper, a self-tuning algorithm for proportional integral derivative (PID) control based on the adaptive interaction (AI) approach theory efficiently used in artificial neural networks (ANNs) is proposed. In this approach, a system is decomposed into interconnected subsystems, and adaptation occurs in the interaction weights among these subsystems. The principle behind the adaptation algorithm is mathematically equivalent to a gradient descent algorithm. The same adaptation as the well-known backpropagation algorithm (BPA) can be achieved without the need of a feedback network, which would propagate the errors, by applying adaptive interaction. Thereby, the ANN controller can be adapted directly without wasting calculation time in order to increase the frequency response of the controller. The velocity control of a brushless DC motor (BLDCM) under slowly and rapidly changing load conditions is simulated to demonstrate the effectiveness of the algorithm. The AI tuning algorithm was used to tune up the PID gains, and the simulation results with PID adaptation process are presented by comparing the obtained results with the adaptive PID controller based on BPNN and a conventional PID controller.