基于智能控制算法的BLDCM调速系统研究

发布时间：2018-03-26 15:57

本文选题：无刷直流电机　切入点：调速　出处：《太原理工大学》2017年硕士论文

【摘要】：无刷直流电机(BLDCM)由于具备构造简单、效率高、控制方便、运行安全可靠等特点,出现后便在军事、汽车驱动、工业自动化、民航等领域取得了普遍的应用。与此同时,由于电子、计算机、自动控制技术的飞速发展,人们也提高了对其调控品质的要求。所以,研究有着反应更快、精度更高、稳定性更好的BLDCM控制系统具备非常广阔的运用前景。本文针对传统的BLDCM调速系统很难满足其在高速、高精度及扰动较大等场合的应用。以智能控制算法为首要的探讨对象,研究可应用于工业焦炉自动测温机器人的BLDCM调速控制系统。根据其对运动有着对位要准、要快、抗干扰能力要强及避免发生滑动等的要求,采用基于改进粒子群算法的模糊PID调速系统对测温机器人的BLDCM运动部分进行调控。首先介绍了BLDCM的组成及其运行原理,并在理想的条件下分析了其数学模型,建立了三相电机仿真模型;紧接着经过拉普拉斯变换后确定了其传递函数且在Simulink平台下搭建了动态仿真结构。然后,分析了模糊控制的基本原理、组成和结构,设计了该算法的逻辑规则库,并将其与PID算法相结合,构成的模糊PID控制算法既保留了PID算法的鲁棒性,又体现出模糊控制的动态调节品质,使得整个被控系统具备一定的自适应能力,展现出模糊PID控制器的优良特性。最后,详细分析了标准粒子群算法的原理、执行流程及其数学表达式,并针对该算法寻优速度不足和容易陷入局部最优的缺点,采用了正交试验机制和模仿染色体变异机制的突变策略对传统粒子群优化算法做了改进。通过举例说明了正交试验对算法寻优速度和效率的提升作用。接着为了使整个算法在迭代的过程中保持良好的多样性,仿照基因突变机理引入突变策略对算法的极值传递过程进行改进,可避免过早发生聚集现象从而克服其陷入局部最优,并就该两种策略的引入给出了改进算法运行的伪代码。经过MATLAB编程实现与用benchmark函数测试的对比实验,证明了所用改进策略的有效性。并将其应用在模糊PID调节器的参数优化问题中,显著提高了控制器的各项调控品质,使得整个BLDCM调速系统具备良好的精确度、响应速度、稳定性以及自适应能力。
[Abstract]:BLDCM (Brushless DC Motor) has been widely used in military, automobile driving, industrial automation, civil aviation and so on, because of its simple structure, high efficiency, convenient control, safe and reliable operation and so on. Because of the rapid development of electronic, computer and automatic control technology, people also improve the quality of their control. Therefore, the research has a faster response and higher accuracy. The BLDCM control system with better stability has a very broad application prospect. In this paper, the traditional BLDCM speed control system is difficult to meet the needs of high speed, high precision and large disturbance. This paper studies the BLDCM speed control system of industrial coke oven automatic temperature measuring robot. According to the requirements of its motion alignment, fast, strong anti-interference ability and avoiding sliding, etc. A fuzzy PID speed regulating system based on improved particle swarm optimization (PSO) is used to control the BLDCM motion of the temperature measuring robot. Firstly, the composition and operation principle of BLDCM are introduced, and its mathematical model is analyzed under ideal conditions. The simulation model of three-phase motor is established, and then the transfer function is determined after Laplace transform and the dynamic simulation structure is built on Simulink platform. Then, the basic principle, composition and structure of fuzzy control are analyzed. The logic rule base of the algorithm is designed and combined with the PID algorithm. The fuzzy PID control algorithm not only retains the robustness of the PID algorithm, but also embodies the dynamic regulation quality of the fuzzy control. It makes the whole controlled system have certain adaptive ability, and shows the excellent characteristics of fuzzy PID controller. Finally, the principle, execution flow and mathematical expression of standard particle swarm optimization algorithm are analyzed in detail. Aiming at the shortcomings of the algorithm, such as insufficient optimization speed and easy to fall into local optimum, The traditional particle swarm optimization (PSO) algorithm is improved by using orthogonal experiment mechanism and mutation strategy which imitates chromosome mutation mechanism. The paper illustrates the effect of orthogonal experiment on the speed and efficiency of optimization. So that the whole algorithm has a good variety of iterations, Based on gene mutation mechanism, mutation strategy is introduced to improve the extremum transfer process of the algorithm, which can avoid premature aggregation and overcome its local optimum. The pseudo code of the improved algorithm is given for the introduction of the two strategies, and the contrast experiment between the MATLAB programming and the test with benchmark function is given. It is proved that the improved strategy is effective, and it is applied to the parameter optimization of fuzzy PID regulator, which improves the control quality of the controller, and makes the whole BLDCM speed control system have good accuracy and response speed. Stability and adaptive ability.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM33;TP273

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