PI Controller of Speed Regulation of Brushless DC Motor Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights

Abstract

The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology and the emergence of new permanent magnet materials. Based on the speed regulation characteristics, speed regulation strategy, and mathematical model of brushless DC motor, a parameter optimization method of proportional-integral (PI) controller on speed regulation for the brushless DC motor based on particle swarm optimization (PSO) algorithm with variable inertia weights is proposed. The parameters of PI controller are optimized by PSO algorithm with five inertia weight adjustment strategies (linear descending inertia weight, linear differential descending inertia weight, incremental-decremented inertia weight, nonlinear descending inertia weight with threshold, and nonlinear descending inertia weight with control factor). The effectiveness of the proposed method is verified by the simulation experiments and the related simulation results.

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

“…The classic PSO process keeps going exploring the best location but the convergence is delayed. A theoretical study [3,19] showed that the inertia weight should be between [0.4, 0.9].…”

“…For example [7][8][9], the Ziegler and Nichols method may provide a high order system with big overshoots, highly oscillatory and longer settling time. To solve these challenges and difficulties, various approaches have been proposed to find optimum PID parameters such as [10] meta-heuristic algorithms [11], differential evolution (DE) [12], Flower Pollination Algorithm (FPA) [13], genetic algorithms (GN) [14], Levenberg-Marquardt Algorithm (LMA) [15], Grey Wolf Optimization Algorithm (GWO) [16], Jaya optimization algorithm (JOA) [17], PSO [18,19], Improved Sine Cosine Algorithm (ISCA) [20]. These are optimization methods that have been introduced to tune the controller parameters for speed control of the DC motor.…”

Wavelet Neural Networks for Speed Control of BLDC Motor

“…The objective function is chosen to minimize the reference constraints. The popular performance standards based on the error condition are integrated absolute error (IAE), integrated of time weight square error (ITSE), and integrated of square error (ISE) that can be estimated theoretically in the frequency domain [31,32,36]. In this chapter, multiobjective functions are utilized based on the integral of the squared error (ISE) criterion and overshoot (M p Þ criterion as follow [37,38]: ei ðÞ¼Di ðÞÀyi ðÞ (12) where y(i) is the system output and D(i) is the desired output, while n is the actual speed and n ref is the desired speed.…”

Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

“…Precise speed regulation has always been a major challenge in the field of BLDC motor drive, which necessitates the design of an efficient controller to achieve optimal performance under varying operating conditions, and during the last few decades, many different controllers have been developed to enhance the performance of BLDC motors. [7][8][9][10][11] Generally, proportional integral (PI), proportional differential (PD), or proportional integral differential (PID) controller is the preferable method for speed control of BLDC motors 7,[10][11][12][13] because of its simple structure, strong robustness, and good applicability. However, the existing uncertainties, nonlinearity, and manually tuned parameters of a typical PI, PD, or PID controller make it difficult to determine the appropriate gains to achieve the optimal performance of the control system.…”

“…However, the existing uncertainties, nonlinearity, and manually tuned parameters of a typical PI, PD, or PID controller make it difficult to determine the appropriate gains to achieve the optimal performance of the control system. 9,13 Therefore, intelligent algorithms such as particle swarm, 7 fuzzy logic control, 10 differential evolution, 11 neural network, 14,15,16 neuro fuzzy, 9 and sliding mode control 17,18 are proposed to adjust the gains of the PID controller so as to improve the performance of the BLDC motors. However, sliding mode control has an inevitable drawback chattering, which leads to the decline in the overall system performance.…”