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Reaserch of Fuzzy Generalized Predictive Control Algorithms in Nonlinear System
Author: ZhaoQun
Tutor: ChenXiPing
School: Lanzhou University of Technology
Course: Control Theory and Control Engineering
Keywords: Predictive Control Fuzzy Generalized Predictive Control T - S fuzzy model Fuzzy Neural Network Ant Colony Algorithm PH neutralization process
CLC: TP13
Type: Master's thesis
Year: 2007
Downloads: 293
Quote: 1
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Abstract
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Predictive control is produced and developed in the late 1970s, a new class of computer control algorithms. Due to its good performance, is easy to implement, robustness, and can easily handle the output constraint control advantages in various fields, especially in a complex chemical process control has been widely used. Linear univariate forecast theory development is more mature, but the actual industrial process often multivariate and nonlinear systems. How to improve predictive control to make it more suitable for the practical application of complex industrial processes, is needed to solve the problem. Therefore, the combination of fuzzy control theory with predictive control mechanism, predictive control to intelligent direction, in order to meet the needs of complex industrial process control, predictive control development trend. Fuzzy predictive control for practical problems in the process of industrial control, in-depth research, fuzzy predictive control algorithm is applied to the PH and process control systems. The main content of the paper can be summarized as follows: 1 reviews the background and significance of the fuzzy control and predictive control, and the combination of the two as the need for a new control algorithm; 2 TS model modeling method for black-box system first clustering method with the least squares method to establish the TS model, and then combined with fuzzy neural network to further optimize the model; For gray-box systems, based on prior knowledge of the ant colony algorithm to optimize the model parameters, and thus to a better fuzzy neural network training initial parameters, the simulation results demonstrate the effectiveness of the method; pieces after 3 resulting TS model predictive control part parameters for linear structural characteristics, the TS model into CARMIA structure of the model, combined with the generalized predictive control, linear predictive control of nonlinear systems, the consequent parameters line adjustment model for slow time-varying nonlinear system, the model reflects the controlled object in a timely manner, and predictive control; 4 final fuzzy predictive control algorithm applied to the strongly nonlinear PH comparison with the traditional PID algorithm and fuzzy predictive control algorithm for robust simulation, this paper, fuzzy predictive control algorithm is validated 's.
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