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.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

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.

Research on tool control system of double cutters experimental platform based on fuzzy neural network predictive control

2021 ◽  
Vol 40 (1) ◽  
pp. 65-76
Author(s):  
Peng Zhou ◽  
Junxing Tian ◽  
Jian Sun ◽  
Jinmei Yao ◽  
Defang Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Signal ◽  
Control Technology ◽  
Fuzzy Neural ◽  
Neural Network Predictive Control

According to the characteristics of the tool hydraulic control system of the double cutters experimental pplatform, intelligent control methodology forecasted by fuzzy neural network is introduced into the control system. The two level control systems of fuzzy neural network predictive control and fuzzy control are designed. The fuzzy neural network predictive controller mainly completes the analysis and control of the speed and pressure in the tool hydraulic system. The speed control signal and pressure control signal from the first level are output to the fuzzy controller. Then, through logical reasoning, the control signal is output and the actuator is driven by the fuzzy controller to complete the control function of the tool system. In this paper, compared with the traditional PID control, the fuzzy neural network predictive control technology has better control accuracy, dynamic response performance and steady-state accuracy. The fuzzy neural network predictive control technology can be used to control the tool hydraulic system of Tunnel Boring Machine.

scholarly journals Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jiemei Zhao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Path Tracking ◽  
Input Delay ◽  
Hydrodynamic Coefficients ◽  
Lyapunov Theorem ◽  
Neural Network Predictive Control

A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

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