小波神经网络在无刷直流电机速度控制中的应用

IF 1.3 Q4 AUTOMATION & CONTROL SYSTEMS
A. L. Saleh, A. Obed, H. H. Qasim, Waleed I. Breesam, Y. Al-Yasir, Nasser Ojaroudi, R. Abd‐Alhameed
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引用次数: 2

摘要

近年来,研究者利用小波函数合成神经网络来构建小波神经网络(WNNs),在隐层中利用小波函数作为sigmoid函数来代替人工神经网络中使用的常规sigmoid函数。小波神经网络继承了小波函数和神经网络的自学习、自适应、时频定位、鲁棒性和非线性等特点。此外,小波函数理论保证了小波神经网络能够准确、快速地模拟非线性系统。在本章中,我们将小波神经网络与PID控制器相结合,开发了一种名为小波神经网络-PID控制器的控制器。该控制器将用于控制无刷直流电动机的速度,以获得比传统控制器技术更好的性能。此外,利用粒子群算法对WNN-PID控制器的参数进行了优化。本章介绍了对该方法的改进,即循环小波神经网络(RWNN)。给出了上述方法的仿真结果并进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wavelet Neural Networks for Speed Control of BLDC Motor
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.
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来源期刊
International Journal of Automation and Control
International Journal of Automation and Control AUTOMATION & CONTROL SYSTEMS-
自引率
41.70%
发文量
50
期刊介绍: IJAAC addresses the evolution and realisation of the theory, algorithms, techniques, schemes and tools for any kind of automation and control platforms including macro, micro and nano scale machineries and systems, with emphasis on implications that state-of-the-art technology choices have on both the feasibility and practicability of the intended applications. This perspective acknowledges the complexity of the automation, instrumentation and process control methods and delineates itself as an interface between the theory and practice existing in parallel over diverse spheres. Topics covered include: -Control theory and practice- Identification and modelling- Mechatronics- Application of soft computing- Real-time issues- Distributed control and remote monitoring- System integration- Fault detection and isolation (FDI)- Virtual instrumentation and control- Fieldbus technology and interfaces- Agriculture, environment, health applications- Industry, military, space applications
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