Abstract

In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.

Highlights

  • Brushless DC (BLDC) motors have a wide application in our life due to their high-power density and high dynamic response

  • The wavelet neural networks (WNNs) is used with the PID controller to make an adapted controller named as the WNN-PID controller

  • This controller is utilized to control the speed of BLDC motor in an extensive range and can stock preferable performance than a traditional controller

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Summary

Introduction

Brushless DC (BLDC) motors have a wide application in our life due to their high-power density and high dynamic response. The BLDC motor is utilized with constant loads, varying loads, and position applications with high accuracy. This motor is generally controlled utilizing electronically commutation by three-phase power semiconductor bridge inverter with rotor position sensors that are required for starting and providing proper firing sequence to turn on the power devices in the inverter bridge. The main objective of this chapter is to develop wavelet neural networks (WNNs) to control the speed of the BLDC motor, and the recurrent wavelet neural network (RWNN). These methods lead to an enhanced dynamic performance of the system of motor drive and are resistant to load perturbations. The learning strategy for the wavelet neural network and PID controller is developed based on PSO algorithm

Wavelet networks
Feedforward wavelet neural network (FFWNN)
Radial basis wavelet neural network (RBWNN)
Conventional wavelet neural network
Recurrent wavelet neural networks (RWNNs)
Particle swarm optimization
Speed control of BLDC motor based on wavelet neural network
WNN-PID controller based on PSO
Design of the structure of WNN-PID controller based on PSO training algorithm
Speed control based on feedforward WNN-PID controller
Speed control based on proposed recurrent WNN-PID controller
Comparison of two methods for speed control of BLDC motor drive
Conclusion
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