發(fā)布時間：2018-04-05 14:20

  本文選題：PID控制器　切入點(diǎn)：參數(shù)整定　出處：《安徽大學(xué)》2017年碩士論文

【摘要】：在當(dāng)今的工業(yè)過程控制中,PID(Proportional Integral Differential)控制器由于結(jié)構(gòu)簡單、易實(shí)現(xiàn)、可靠性高、魯棒性能高等特點(diǎn),因此被廣泛的應(yīng)用。根據(jù)相關(guān)的統(tǒng)計資料顯示,實(shí)際的工業(yè)過程中,使用PID控制思想的控制器占95%以上。眾所周知,PID控制器性能的優(yōu)劣直接與PID控制器的參數(shù)相關(guān)聯(lián)。然而隨著現(xiàn)代工業(yè)技術(shù)的飛速發(fā)展,工業(yè)控制系統(tǒng)變的越來越復(fù)雜,傳統(tǒng)的整定PID控制器參數(shù)的算法已經(jīng)不能很好的適應(yīng)于現(xiàn)代越來越復(fù)雜的控制問題。但隨著人工智能領(lǐng)域的不斷發(fā)展和計算機(jī)技術(shù)不斷向自動化領(lǐng)域的滲透,涌現(xiàn)了大量智能算法整定P1D控制器參數(shù)的方法。螢火蟲算法屬于智能優(yōu)化算法中的一種,因其并行性、自組織、易實(shí)現(xiàn)、分布式和魯棒性等特點(diǎn)被廣泛使用。本文將用改進(jìn)的螢火蟲算法(Glowworm Swarm Optimization,GSO)整定PID控制器參數(shù),研究的主要內(nèi)容概述如下:1、本文提出了一種基于導(dǎo)向機(jī)制和自適應(yīng)步長的螢火蟲算法(Based on Directed mechanism and Adaptive-step mechanism GSO,D-AGSO)。一方面,本文在算法中引入了自適應(yīng)步長機(jī)制,使步長可以在算法迭代前期保持較大值,以便可以全局內(nèi)搜索最優(yōu)解,防止算法過早成熟,陷入局部最優(yōu)。在迭代后期,使步長可以保持較小值,防止算法跳過最優(yōu)解或者出現(xiàn)震蕩現(xiàn)象,更加有利于精確尋找最優(yōu)解;另一方面,對于基本的螢火蟲算法,如果螢火蟲個體在其自身動態(tài)決策域半徑內(nèi)沒有找到比自己更亮的螢火蟲,則這類螢火蟲將不確定隨機(jī)移動。因此,這類螢火蟲雖然付出了大量計算代價,但卻沒有發(fā)生較好的位置移動,并且存在導(dǎo)致算法陷入局部最優(yōu)的危險且不利于算法的快速收斂。為了減小這種算法缺陷,本文針對這類的螢火蟲個體,采取了導(dǎo)向性的移動策略,以加快迭代速度和求解精度。為了驗證改進(jìn)算法的有效性與可行性,本文算法與基本的螢火蟲算法、自適應(yīng)步長的螢火蟲算法(Enhanced Glowworm Swarm Optimization Algorithm,EGSO)、基于熒光因子與覓食行為的螢火蟲算法(Foraging-behavior Adaptive-step Glowworm Swarm Optimization Algorithm,FA-GSO)進(jìn)行實(shí)驗比較,結(jié)果證明本文改進(jìn)的螢火蟲算法具有較優(yōu)的尋優(yōu)性能。2、本文提出了基于一種新的改進(jìn)的螢火蟲算法整定PID控制器參數(shù)的方法,并且使用MATLAB中的Simulink工具建立PID控制器仿真模型,對本文提出的方法進(jìn)行仿真實(shí)驗。本文使用四種不同類型的被控對象來驗證改進(jìn)的螢火蟲算法整定PID控制器參數(shù)的方法的性能,并且本文引入經(jīng)典Z-N公式整定方法、粒子群算法整定方法、差分進(jìn)化整定方法與本文的算法進(jìn)行比較,仿真結(jié)果證明本文提出的算法取得了較好的控制效果。3、本文使用改進(jìn)的ITAE評價函數(shù)來驗證本文提出的算法,仿真實(shí)驗證明通過改變評價函數(shù)中的性能權(quán)重系數(shù),本文算法可有效的使PID控制器偏重于某一特定指標(biāo)性能。因此本文算法可以整定出工業(yè)控制過程中所需要的具有針對性的PID控制器。
[Abstract]:In the industrial process control, PID (Proportional Integral Differential) controller has the advantages of simple structure, easy realization, high reliability, high robustness, it has been widely applied. According to the statistics show that the real industrial process, the controller can control the thought of PID accounted for more than 95%. As everyone knows, associated the performance of the PID controller and the PID controller parameters directly. However, with the rapid development of modern industrial technology, industrial control systems are becoming more and more complex, the traditional PID controller parameter tuning algorithm has not well adapted to the control problem more complex in modern more. But with the continuous development of computer technology and artificial intelligence the field continues to permeate the field of automation, the emergence of a large number of intelligent algorithm method for tuning parameters of P1D controller. The firefly algorithm belongs to intelligent optimization algorithm In one, because of its parallelism, self-organization, easy to realize, the characteristics of distributed and robust is widely used. This paper will use the firefly algorithm (Glowworm Swarm Optimization, GSO) PID controller tuning parameters, the main contents of the study are as follows: 1, this paper presents a firefly the guide mechanism and algorithm based on adaptive step (Based on Directed mechanism and Adaptive-step mechanism GSO, D-AGSO). On the one hand, this paper introduces adaptive step mechanism in the algorithm, the algorithm in the early iteration step can keep larger values, so that you can search global optimal solution, to prevent the algorithm premature, falling into the local optimum. In the iteration later, the step can maintain a smaller value, to prevent the algorithm skip the optimal solution or shock phenomenon, more conducive to the exact optimal solution; on the other hand, the firefly algorithm basic, if Firefly individuals did not find more than their bright fireflies in its own dynamic decision domain radius, then the firefly will not determine the random movement. Therefore, this kind of firefly although pay a lot of computation cost, but did not move a good position and fast convergence of the algorithm into a local danger guide the best and not conducive to the algorithm. This algorithm in order to reduce defects, according to this kind of firefly individual, take mobile strategy oriented, to accelerate the convergence speed and accuracy and feasibility. In order to verify the effectiveness of the improved algorithm, the firefly algorithm in this paper and the basic of the firefly algorithm adaptive step (Enhanced Glowworm Swarm Optimization Algorithm, EGSO), firefly algorithm based on fluorescence factor and foraging behavior (Foraging-behavior Adaptive-step Glowworm Swarm Optimization Algorit HM, FA-GSO) were compared. Results show that the improved firefly algorithm has better optimization performance of.2, this paper proposed a new method to improve the firefly algorithm tuning parameters of PID controller based on MATLAB, and use the Simulink tools to build PID controller simulation model, the simulation experiment of the the method proposed in this paper. The performance of the four different types of objects to verify the firefly algorithm to improve the method of tuning the parameters of PID controller, and introduced the classical Z-N formula tuning method, particle swarm optimization tuning method, compare the differential evolution setting method and the simulation results show that this algorithm. The proposed algorithm has.3 better control effect, this paper uses ITAE evaluation function to verify the improved algorithm proposed in this paper, simulation results show that by changing the performance evaluation function in the Weight coefficient, this algorithm can effectively make the PID controller bias the performance of a specific index. Therefore, this algorithm can set the pertinent PID controller needed in the process of industrial control.

【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP273

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本文編號：1715152