<span>In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain an novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting and self-learning. The suggested controller method is utilizing to control the speed of BLDC motor of which supply a better showing than utilizing classical controllers with a wide range of control. The proposed controller parameters are matched continuously using Particle Swarm Optimization (PSO) algorithm. The simulation results based on proposed DWNN-PID controller demonstrate a superior in the stability and performance compared at utilizing classical WNN-PID and conventional PID controllers. The simulation results are accomplished using Matlab/Simulink. It shows that the proposed control scheme has a superior performance.</span>
This paper exhibits a design procedure for tuning the parameters of Fractional Order Proportional Integral Derivative (FOPID) P controller to optimize the DC motor drive operation. The optimization technique is establishing on Invasive Weed Optimization (IWO). This paper also proposes the use of anti-windup aspect to against the saturation which may occur in the FOPID controller. The objective of this design is to improve the performance of the drive subjected to different transient response and loading conditions. A comparative study is carried out with a classical PID controller. The Matlab simulation results show more improvements in the proposed system.
As a result of increasing the use of the brushless direct current (BLDC) motor in many life applications instead of the traditional motors, it is important to list and specify the more for its controlling methods. This paper presents a number of speed and current controlling methods as hysteresis band, variable dc-link bus voltage and pulse width modulation (PWM) controlling methods. These controlling methods have proportional integral derivative (PID) gains which are optimized by using particle swarm optimization (PSO) algorithm. By using fast Fourier transform (FFT) analysis to study the controller behavior from frequency analysis of the output signals and compute total harmonic distortion (THD), it can specify the more useful controlling method. The framework is modeled and fabricated by using Matlab/Simulink.
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