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The Research of Nonlinear Predictive Control Based on Inverse System Method

Author: HuangLuJiang
Tutor: LiHua
School: Lanzhou Jiaotong University
Course: Control Theory and Control Engineering
Keywords: Inverse System Method Dynamic Matrix Control Generalized Predictive Control Neural Networks Least squares support vector machine
CLC: TP13
Type: Master's thesis
Year: 2009
Downloads: 211
Quote: 2
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Abstract


Predictive control method based on inverse system control problem for nonlinear systems, in-depth study. The inverse system is a feedback linearization method, nonlinear system inverse model presupposes the existence of to construct nonlinear system inverse model and the inverse model and the original system in series to form a pseudo-linear system, complete nonlinear systems of linear processing. Thus predictive controller based on pseudo-linear system design to achieve predictive control of nonlinear systems. This article focuses on the following aspects of research: (1) nonlinear system modeling method based on inverse system. Nonlinear system inverse model presupposes the existence of data by sampling the input and output of the nonlinear system, using BP neural network offline training nonlinear system inverse model. A large number of simulation study found that the presence of large errors due to poor training and generalization ability of BP neural network, resulting in the establishment of pseudo-linear system model. Therefore, further analysis based on least squares support vector machine modeling method effectively overcomes the defects of BP neural network modeling. The simulation results show that the least squares support vector machine with higher accuracy and better generalization ability than the BP neural network inverse system modeling. (2) based on the method of inverse system controller design of the single-variable nonlinear systems. Outside interference and internal parameter changes for the pseudo-linear system robustness, design the PID dynamic matrix control and generalized predictive controller. Theoretical analysis and simulation results show that, although the PID controller can suppress the interference of pseudo-linear system, but there is no control over the difficult problem of lag system and the optimal parameter adjustment. Therefore, were designed for the pseudo-linear system dynamic matrix controller and generalized predictive controller. Further analysis and simulation prove, disturbance and parameter changes for a variety of pseudo-linear system, two controllers can be very good suppression and achieved good static and dynamic performance and robustness. (3) multi-variable nonlinear control system design based on inverse system method. On the basis of single-variable nonlinear systems control, multi-variable nonlinear systems is discussed. Constructed multivariate pseudo-linear system based on multivariable nonlinear coupling system stripped the multiple univariate linear system, and design a dynamic matrix controller group. Found through simulation, the multivariate inverse system method of decoupling and linearization effect is limited. Through the design of the dynamic matrix controller group can make up for the error in the pseudo-linear system of tectonic processes and achieve better control effect. Through theoretical studies and simulation analysis of the text, the nonlinear predictive controller design method based on inverse system method can control policy is applied to the prediction of the linear system of nonlinear systems, and can achieve good control effect. Prove the effectiveness of the method, a novel control strategy for nonlinear model predictive control.

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