基于支持向量机的非线性系统辨识与预测控制研究
文献类型：学位论文
| 作者 | 向立志 |
| 学位类别 | 工学博士 |
| 答辩日期 | 2006-05-30 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 中国科学院自动化研究所 |
| 导师 | 高东杰 ; 邹益仁 |
| 关键词 | 最小二乘支持向量机 Hammerstein非线性模型 非线性系统辨识 非线性预测控制 锅炉燃烧控制系统 Least Square Support Vector Machines Hammerstein Nonlinear Model Nonlinear System Identification Nonlinear Model Predictive Control Boiler Combustion Control System |
| 其他题名 | Studies on Nonlinear System Identification and Nonlinear Model Predictive Control Based on Support Vector Machines |
| 学位专业 | 控制理论与控制工程 |
| 中文摘要 | 本文致力于“基于支持向量机的非线性系统辨识与预测控制研究”，旨在将支持向量机理论、系统辨识理论与预测控制理论结合起来，形成一套新的解决非线性控制问题的思路与办法。主要的研究工作与贡献如下： 1）系统回顾了支持向量机理论、系统辨识理论和预测控制理论各自的基本原理和典型算法，详细研究了三者之间的内在联系，分析找到了将支持向量机方法用于非线性系统辨识和非线性预测控制的切入点，从而为论文的研究工作奠定了理论基础。 2）研究了基于最小二乘支持向量机的非线性模型辨识算法。利用Hammerstain模型结构，在对象的线性动态模型的输入端加入静态非线性环节并用最小二乘支持向量机非线性回归函数表示，从而形成了基于最小二乘支持向量机的NARX非线性模型辨识算法与N4SID非线性模型辨识算法，并分别详细推导了这两种非线性辨识算法的优化解法。 3）研究了基于最小二乘支持向量机的非线性预测控制算法。用辨识算法获得的基于支持向量机的非线性NARX模型和非线性状态空间模型作为预测模型，引入非线性预测控制的性能指标函数中，构成非线性滚动优化问题，运用非线性规划算法（如拟牛顿法等）迭代求解这类滚动优化问题获取非线性预测控制律，初步实现了两种新的基于支持向量机的非线性NARX模型预测控制算法和基于支持向量机的非线性状态空间模型预测控制算法。 4）研究了基于支持向量机的非线性系统辨识与预测控制在锅炉燃烧系统中的应用。用基于支持向量机的非线性系统辨识算法对锅炉燃烧系统进行非线性动态建模，成功建立了基于支持向量机的三输入三输出非线性模型，在此基础上将该模型作为预测模型，构造三输入三输出的非线性预测控制回路，最终利用基于支持向量机的非线性预测控制算法成功的实现了锅炉燃烧系统的控制。 |
| 英文摘要 | This dissertation is given attention to the “studies on the nonlinear system identification and nonlinear model predictive control based on support vector machines”. The aim of the research in this dissertation is coming into being a set of new thoughts and means to deal with some nonlinear control problems by joining the support vector machines theory, the system identification theory and the model predictive control theory together. The main study work and contributions are as follows: 1) The basic fundamentals and typical algorithms of support vector machines theory, system identification theory and model predictive control theory are systematically reviewed; the inherent relationship among the three theories is particularly investigated; by analyzing the breakthrough point for how to use the support vector machines to assist the nonlinear system identification and nonlinear model predictive control is found out; all these work is to establish the basis of the subsequent research in the dissertation. 2) Two nonlinear system identification algorithms based on least square support vector machines (LS-SVM) is studied. According to the Hammerstein nonlinear model structure, a static nonlinearity is added to input port of the linear dynamic systems such as the ARX (Auto Regressive with eXternal input) model or the state space model. The nonlinearity is estimated by LS-SVM regression and the parameters of the linear dynamic parts are estimated by Lagrange optimization, as a result two nonlinear identification algorithms: the NARX (Nonlinear ARX) identification based on LS-SVM and the nonlinear N4SID (Numerical algorithms for Subspace State Space System Identification) identification based on LS-SVM are derived in detail. 3) Two nonlinear model predictive control algorithms, the nonlinear NARX model predictive control and the nonlinear state space model predictive control which based on least square support vector machines, are studied. The nonlinear ARX identification model based on LS-SVM or the nonlinear state space identification model based on LS-SVM is substituted into the quadratic objective function as a predictive model to construct a nonlinear receding horizon optimization problem. To deriving the control law, how to solve the optimization problem is then inferred in detail using effective programming algorithms such as quasi Newton methods etc. 4) The application of the nonlinear system identification and nonlinear model predictive control based on support vector machines in the boiler combustion control is studied. Firstly a nonlinear three-input-three-output model for the boiler combustion system is obtained by the nonlinear system identification methods based on support vector machines, then a tree-input-three-output predictive control loop is establish, finally the control objective is achieved by the nonlinear model predictive control algorithms based on support vector machines. |
| 语种 | 中文 |
| 其他标识符 | 200418014690005 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/5925]  |
| 专题 | 毕业生_博士学位论文 |
| 推荐引用方式 GB/T 7714 | 向立志. 基于支持向量机的非线性系统辨识与预测控制研究[D]. 中国科学院自动化研究所. 中国科学院研究生院. 2006. |
入库方式： OAI收割
来源：自动化研究所
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