A Novel Generalized Predictive Control Algorithm with Online Tuning Maximal Output Increment Based on BP Neural Network

2009 ◽  
Author(s):  
Xialai Wu ◽  
Aiping Yang ◽  
Wenzhan Dai
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Algorithm ◽  
Generalized Predictive Control ◽  
Maximal Output ◽  
Online Tuning

Study on Control of the Second Carbonation in the Clarifying Process of Sugar Cane Juice via Generalized Predictive Control Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1257-1260 ◽  
Author(s):  
Chun Ning Song ◽  
Wen Han Zhong
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Sugar Cane ◽  
Control Algorithm ◽  
Generalized Predictive Control ◽  
Cane Juice ◽  
Sugar Cane Juice ◽  
High Control ◽  
Complex Nonlinear Systems

The second carbonation in the clarifying process of sugar cane juice is a dynamic nonlinear system which has the characteristics of strong non-linearity, multi-constraint, large time-delay, multi-input and other characteristics of complex nonlinear systems. In this paper, BP neural network is applied to the model of the second carbonation clarifying process of sugar cane juice. The generalized predictive control algorithm is employed to the optional control of color value in clarifying process of second carbonation. The result of Matlab simulation shows that generalized predictive control algorithm based on BP neural network implement the optimal control of the second carbonation with strong robustness and high control precision.

Application of Model Predictive Control Based on BPNN to Heating Boiler

2011 ◽  
Vol 383-390 ◽  
pp. 2242-2248
Author(s):  
Yan Ping Wang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Algorithm ◽  
Closed Loop ◽  
Control Method ◽  
Feedback Information ◽  
Heating Boiler ◽  
Controlled Object

This paper presents the algorithm of model predictive control (MPC) based on BP neural network to the burden system of the heating boiler. Because the burden system of the heating boiler is complex, the proposed approach uses steady, effective way to control the boiler. There is a closed-loop, repeating online optimization, model-based control algorithm which deals with the feedback information and the quantity of the fuel entering the boiler by the way of multi-step future predicting and compensating based on BP neural network. By simulation, it is demonstrated that the burden system of the heating boiler using MPC as control method is better in performance than the traditional PID. Besides, it is compliant to the model of the controlled object, especially to those which parameters of the model are variable.

Generalized Predictive Control of Flatness Using BP Neural Network

1999 ◽  
Vol 32 (2) ◽  
pp. 7073-7077
Author(s):  
Zhe Wang ◽  
Hongshuang Di ◽  
Guodong Wang ◽  
Xianghua Liu
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Generalized Predictive Control

Neural Network Predictive Control for Piezoelectric Smart Structures

2008 ◽  
Author(s):  
Jingjun Zhang ◽  
Ercheng Wang ◽  
Ruizhen Gao
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Control System ◽  
Predictive Control ◽  
Control Algorithm ◽  
Smart Structure ◽  
Generalized Predictive Control ◽  
The Neural Network ◽  
Nonlinear Generalized Predictive Control ◽  
Neural Network Predictive Control

The piezoelectric smart structure is a force-electric coupling structure, and piezoelectric patches can not be patched ideally, so it is difficult to build the accurate mathematical model of piezoelectric smart structure. The traditional vibration control methods depend on the structural mathematical model, and the control result is unsatisfactory. Considering this problem, this paper introduces the nonlinear generalized predictive control algorithm based on neural network predictive model into piezoelectric smart structure. Because of the difficulties of building the mathematical model and extracting dynamic data from experiment, the finite element software (ANSYS) is employed to analyze and obtain the dynamic response data of piezoelectric smart structure through modal analysis and transient analysis. Neural network predictive model of structure is built through off-line training on the basis of the data. The nonlinear generalized predictive control based on neural network has a better ability to solve complex nonlinear problem. Then the author introduces the Neural Network Based System Identification Toolbox (NNSYSID) and Neural Network Based Control System Design Toolkit (NNCTRL), which are two special toolboxes for designing neural network control system and can save lots of time for designers who can commit themselves to sixty-four-dollar question. At last, the author shows the method through a case. A cantilever beam which surface is boned piezoelectric patches used for sensor and actuator respectively is analyzed by ANSYS and controled by the neural network predictive control algorithm on the platform of NNSYSID and NNCTRL. This is a simple and effective method for designers to solve the vibration control problem of piezoelectric smart structure.

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