An optimal PID controller parameters for a brushless DC motor by using Newton-Raphson-based optimizer algorithm

The brushless DC (BLDC) motor was developed to address the shortcomings of DC motors, whose driving performance is known for being more effective and requiring less upkeep. PID (Proportional, Integral, and Derivative) control systems are broadly used in many electrical engineering applications, including the control of brushless DC motors in general desired output quantities like voltage, current, speed, and position. It is essential to comprehend these systems in order to optimize their parameters and achieve the desired system performance. The Ziegler-Nichols (ZN) methods are one way to adjust PID settings. The first step is to plot the open loop's step response using the model's tangent line to determine the final gain and ultimate time, the alternative approach involves gradually raising the gains until the system becomes unstable, after which the gains are adjusted. Rapid convergence to near-optimal solutions without becoming trapped in local optima is one of artificial intelligence's primary advantages. The aim of this paper is to use MATLAB simulation to tune the PID controller for brushless DC motor speed control using a metaheuristic algorithm for global optimization called The Newton-Raphson-Based Optimizer (NRBO), where the objective function is the integral of squared time multiply squared error (ITSES). A comparison in optimal performances (rise time, settling time, overshoot, peak response, and peak time) are compared with a work that had applied optimization by Genetic Algorithm and Simulated annealing on the same model and parameters of brushless DC motor.