【作者】 王斌；

【导师】 李士勇；

【作者基本信息】 哈尔滨工业大学 ， 控制理论与控制工程， 2006， 硕士

【摘要】 近年来,医学免疫学的飞速发展引起了许多领域当中研究人员的密切关注。研究人员不断地把生物免疫系统当中强大的信息处理机制用于解决科学与工程当中的实际问题,这就产生了人工免疫系统。由于生物免疫系统具有高度的智能以及人工免疫系统与生物免疫系统的密切关系,因此人工免疫系统已经成为当前的一个研究热点。如果将人工免疫系统与控制系统相结合,就会给控制系统带来更多的智能特性,同时也会对控制领域的发展起到推动的作用。本文对生物免疫系统的主要生理功能—免疫应答进行了深入的学习和分析,并重点研究了免疫应答当中的适应性免疫应答。然后根据适应性免疫应答的过程提取出了免疫优化原理和免疫反馈原理,并把它们同控制系统相结合。本文的研究属于人工免疫系统的范畴。首先,为了解决PID控制器的参数优化问题,本文根据免疫优化原理设计了一种免疫优化算法。由于适应性免疫应答当中的优化过程实际上就是B细胞的进化过程,因此这种免疫优化算法就是模拟B细胞的进化过程而设计出来的。根据B细胞的进化过程,本文将算法分为以下几个步骤:选择、近程跳变、远程跳变、更新、记忆和算法参数调整。仿真结果表明了这种算法的有效性,而且这种算法在性能上也要优于一种典型的遗传算法。接着,本文将免疫反馈原理同PID控制相结合,将误差和误差的变化作为抗原,并结合积分作用的特点,设计了一种模糊免疫非线性PID控制器,随后采用免疫优化算法对其进行参数优化设计。仿真结果表明了这种控制器要优于PID控制器,从而提高了PID控制器的性能。最后,为了解决模糊控制器的设计问题,本文以单级倒立摆为被控对象,设计了一种将两个采用Sugeno模糊推理方法的二维模糊控制器并联而成的模糊控制器,它具有结构简单、控制算法易于实现、计算效率高的优点。接着采用免疫优化算法对其进行参数优化设计。仿真结果表明了此控制方法的有效性和可行性。更多还原

【Abstract】 In recent years, the fast development of medicine immunology attracts many researchers in different fields. The researchers continually apply the powerful information processing mechanisms in the biology immune system into science and engineering fields for solving actual problems, so the artificial immune system is born. Because there is high intelligence in the biology immune system and the close relationship between the biology immune system and the artificial immune system, the artificial immune system have been a research hotspot. If we integrate the artificial immune system with the control system, then we can bring more intelligence into the control system, and promote the development of the control field.This paper learns and analyses the main physiological function of the biology immune system—immune response deeply, and researches on the process of the adaptive immune response in the immune response. On the basis of the process of the adaptive immune response, this paper abstracts the immune optimization principle and the immune feedback principle, then integrates them with the control system. The research of this paper belongs to the artificial immune system.First, to solve the parameters optimization problems in the PID controller, this paper designs an immune optimization algorithm based on the immune optimization principle. Because the optimization process in the adaptive immune response actually is a process of the evolution of B cells, this algorithm is designd based on the process of the evolution of B cells. The steps of this algorithm are as follows: selection, short-range variation, long-distance variation, update, remembrance and the parameters in the algorithm adjustment. The simulation results show the validity of this algorithm, and the mechanism of this algorithm is superior to a typical genetic algorithm.Then, this paper integrates the immune feedback principle with PID control. Comparing the error and the variation of the error with the antigens, this paper designs a fuzzy immune nonlinear PID controller according to the character of the integral control action, and then gets the parameters of this controller by更多还原