Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor

Speed regulation is one of the significant characteristics to be adopted in the field of brushless DC motor drive for effective and accurate speed and position control operations. In this paper, stability analysis and performance characteristics of brushless direct current motor are studied and implemented with a new deep learning neural network—fuzzy-tuned proportional integral derivative (PID) speed controller. Deep learning architecture is designed for the multi-layer perceptron network, and the output from the neural module fires the rules of the fuzzy inference system mechanism. The parameters of deep perceptron neural network (DPNN) are tuned for near optimal solutions using the unified multi-swarm particle swarm optimization, and in turn the optimized DPNN selects the parameters of the fuzzy inference system. Deep learning neural network with the fuzzy inference system tunes the gain values of the PID controller and performs an effective speed regulation. The performance characteristics of the designed speed controller are tested for a step change in input speed and also for impulsive load disturbances. Further, the stability analysis of the new proposed controller is investigated with Lyapunov stability criterion by deriving the positive definite functions. The weight parameters of DPNN model and the number of rules of fuzzy system are tuned for their near optimal solutions using multi-swarm particle swarm optimization. From the results, it is well proven that the proposed controller is more stable and guarantees consistent performance than other considered controllers in all aspects. Simulation-based comparisons illustrate that the design methodologies outperform other controller designs from the literature.