基于支持向量機建模的非線性預測控制研究
發(fā)布時間：2018-08-24 16:26
【摘要】：針對化學工業(yè)中經(jīng)常出現(xiàn)的純延遲對象以及非最小相位對象,傳統(tǒng)的控制方法如PID控制,LQ最優(yōu)控制的控制效果都不夠令人滿意,在面對多變量系統(tǒng)也無能為力。預測控制算法的出現(xiàn)正好解決了這個難題,在面對操作變量和被控變量維數(shù)很高,需要滿足物理約束,時滯系統(tǒng)等問題中,預測控制具有十分明顯的優(yōu)勢。但是隨著過程工業(yè)日益大型化、連續(xù)化,現(xiàn)代工業(yè)生產過程日益復雜,此時,線性預測控制方法就不能很好的完成控制任務,而非線性預測控制又存在在線計算量大的問題。本文主要針對非線性預測控制,利用支持向量機進行建模,提出了一種在每一個采樣點進行局部線性化的方法,把在線計算最優(yōu)解問題轉化為求解一個二次規(guī)劃問題,通過這樣的方式減少了非線性預測控制在線計算量大的問題,并保證了控制的精確度和穩(wěn)定性。 本文的研究工作主要為以下幾方面： 1.預測控制和支持向量機的內容研究 本文對預測控制的基本原理、發(fā)展過程和主要特點進行了簡明的介紹,同時詳細的闡述了支持向量機的基礎理論,包括支持向量機的基本原理以及針對分類和回歸兩種不同用途的介紹和對比。 2.基于SVM-Wiener模型的非線性預測控制研究 論文詳細的闡述了SVM-Wiener模型的建模過程,包括動態(tài)線性部分和靜態(tài)部分非線性部分的詳細分析和研究。并給出了非線性預測控制器的設計流程,重點介紹和分析了非線性預測控制局部線性化的計算方法。 3.基于SVM的Hammerstein-Wiener模型非線性預測控制研究 論文簡單介紹了Hammerstein-Wiener模型的結構特點,詳細推導了基于SVM的Hammerstein-Wiener模型的建模過程。根據(jù)模型的結構和特點設計了非線性預測控制器。 4.基于連續(xù)攪拌釜式聚合反應器的仿真 在論文的最后,對文章介紹的非線性預測控制方法進行了基于連續(xù)攪拌釜式聚合反應器的仿真,并通過大量的數(shù)據(jù)分析和仿真圖對比,證明了所提出方法的控制精確度、穩(wěn)定性以及高效性。 本文中有各類結構圖、仿真圖、流程圖等共29張,進行數(shù)據(jù)對比以及定義參數(shù)等表共8張,引用參考文獻52篇。
[Abstract]:The traditional control methods such as PID control and LQ optimal control are not satisfactory for the pure delay object and non-minimum phase object which often appear in the chemical industry and can not be used in the face of multivariable system. The emergence of predictive control algorithm solves this problem. In the face of the high dimension of operational variables and controlled variables, it needs to meet the physical constraints, time-delay systems and other problems, predictive control has a very obvious advantage. However, with the process industry becoming larger and more continuous, the modern industrial production process is becoming more and more complex. At this time, the linear predictive control method can not complete the control task well, and the nonlinear predictive control has the problem of large amount of on-line calculation. In this paper, for nonlinear predictive control, support vector machine (SVM) is used to model, and a method of local linearization at every sampling point is proposed. The problem of online optimal solution is transformed into a quadratic programming problem. In this way, the problem of large online computation of nonlinear predictive control is reduced, and the accuracy and stability of the control are guaranteed. The main work of this paper is as follows: 1. Research on the content of Predictive Control and support Vector Machine in this paper, the basic principle, development process and main characteristics of predictive control are briefly introduced. At the same time, the basic theory of support vector machine is expounded in detail. Including the basic principles of support vector machines and the classification and regression for two different uses of the introduction and comparison. 2. Research on nonlinear Predictive Control based on SVM-Wiener Model the modeling process of SVM-Wiener model is described in detail, including the detailed analysis and research of dynamic linear part and static part nonlinear part. The design flow of nonlinear predictive controller is given, and the calculation method of local linearization of nonlinear predictive control is introduced and analyzed emphatically. Research on nonlinear Predictive Control of Hammerstein-Wiener Model based on SVM in this paper, the structural characteristics of Hammerstein-Wiener model are briefly introduced, and the modeling process of Hammerstein-Wiener model based on SVM is deduced in detail. According to the structure and characteristics of the model, a nonlinear predictive controller is designed. At the end of the paper, the nonlinear predictive control method introduced in this paper is simulated based on the continuous agitator polymerization reactor. The control accuracy, stability and efficiency of the proposed method are proved by a large number of data analysis and simulation diagram comparison. In this paper, there are 29 structural diagrams, simulation diagrams and flowcharts, 8 tables for data comparison and definition of parameters, and 52 references.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB114.2
本文編號：2201369
[Abstract]:The traditional control methods such as PID control and LQ optimal control are not satisfactory for the pure delay object and non-minimum phase object which often appear in the chemical industry and can not be used in the face of multivariable system. The emergence of predictive control algorithm solves this problem. In the face of the high dimension of operational variables and controlled variables, it needs to meet the physical constraints, time-delay systems and other problems, predictive control has a very obvious advantage. However, with the process industry becoming larger and more continuous, the modern industrial production process is becoming more and more complex. At this time, the linear predictive control method can not complete the control task well, and the nonlinear predictive control has the problem of large amount of on-line calculation. In this paper, for nonlinear predictive control, support vector machine (SVM) is used to model, and a method of local linearization at every sampling point is proposed. The problem of online optimal solution is transformed into a quadratic programming problem. In this way, the problem of large online computation of nonlinear predictive control is reduced, and the accuracy and stability of the control are guaranteed. The main work of this paper is as follows: 1. Research on the content of Predictive Control and support Vector Machine in this paper, the basic principle, development process and main characteristics of predictive control are briefly introduced. At the same time, the basic theory of support vector machine is expounded in detail. Including the basic principles of support vector machines and the classification and regression for two different uses of the introduction and comparison. 2. Research on nonlinear Predictive Control based on SVM-Wiener Model the modeling process of SVM-Wiener model is described in detail, including the detailed analysis and research of dynamic linear part and static part nonlinear part. The design flow of nonlinear predictive controller is given, and the calculation method of local linearization of nonlinear predictive control is introduced and analyzed emphatically. Research on nonlinear Predictive Control of Hammerstein-Wiener Model based on SVM in this paper, the structural characteristics of Hammerstein-Wiener model are briefly introduced, and the modeling process of Hammerstein-Wiener model based on SVM is deduced in detail. According to the structure and characteristics of the model, a nonlinear predictive controller is designed. At the end of the paper, the nonlinear predictive control method introduced in this paper is simulated based on the continuous agitator polymerization reactor. The control accuracy, stability and efficiency of the proposed method are proved by a large number of data analysis and simulation diagram comparison. In this paper, there are 29 structural diagrams, simulation diagrams and flowcharts, 8 tables for data comparison and definition of parameters, and 52 references.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB114.2
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相關期刊論文 前10條
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