The application of neural network α-th order inverse control method in boiler-turbine coordinated control system

2010 ◽  
Author(s):  
Lingfang Sun ◽  
Yuheng Zhang ◽  
Li Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Coordinated Control ◽  
Inverse Control

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

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.

Coordinated Control System Modeling of Ultra-Supercritical Unit based on a New Fuzzy Neural Network

Energy ◽  
2021 ◽  
pp. 121231
Author(s):  
Guolian Hou ◽  
Jian Xiong ◽  
Guiping Zhou ◽  
Linjuan Gong ◽  
Congzhi Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
System Modeling ◽  
Coordinated Control ◽  
Fuzzy Neural

Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

2013 ◽  
Vol 291-294 ◽  
pp. 2416-2423 ◽  
Author(s):  
Guo Duo Zhang ◽  
Xu Hong Yang ◽  
Dong Qing Lu ◽  
Yong Xiao Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nuclear Reactor ◽  
Control Method ◽  
Setting Time ◽  
Pressure Control ◽  
Network Control ◽  
Pressure System

The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.

scholarly journals JUSTIFICATION OF PARAMETERS OF THE CONTROL SYSTEM OF ELECTROMECHANICAL DEBALANCE VIBRODRIVE OF VIBRATION MACHINES ON THE BASIS OF ARTIFICIAL NEURAL NETWORK

2021 ◽  
pp. 52-63
Author(s):  
Leonid Yaroshenko ◽  
Roman Chubyk ◽  
Iryna Derevenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Mechanical System ◽  
Predictive Model ◽  
Control Method ◽  
Resonant Mode ◽  
Neural System ◽  
Artificial Neural ◽  
Vibration Parameters

The article analyzes and proposes an approach to the construction of a control system for electromechanical debalance vibrodrive for vibration machines based on an artificial neural network. As a result of the analysis of various methods of managing dynamic objects it is concluded that the most appropriate and perfect for this type of machine is neurocontrol method of predictive model neurocontrol, which allows to expand the functionality of these vibrating machines and significantly save energy for vibratory drive of their oscillations. A direct neuro-emulator is used to predict the future behavior of the oscillating mechanical system of the vibration technological machines and to calculate errors. An important feature of the predictive neurocontrol model in the proposed method of controlling the operation of vibrating technological machines using an artificial neural system is that there is no neurocontroller that needs to be trained, its place is taken by the optimization algorithm. Applying the proposed method of controlling operation of adaptive vibration technology machines using artificial neural network will optimize the electromechanical control of debalanced vibration drive of vibrating machines and provide optimal resonant modes of its operation (which is energy efficient) in all technological modes of vibrating operation. The technical and economic characteristics of this control method are further improved due to the fact that the proposed control method uses the technology of predictive model neurocontrol and as a result is constantly calculated (forecasted) several cycles in advance and determines the best strategy to control the frequency of forced cyclic vibration. As a result, the mechanical system of vibration machines spends less time in non-resonant mode. This method of control also minimizes the duration of transients when changing the load mass of the working body vibration or changing the mode of vibration parameters and the parameters of their technological process.

Vibration Control of the Piping System: A Method Using Feedback-Error-Learning

1995 ◽  
Author(s):  
Yoshihiro Takita
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Method ◽  
Piping System ◽  
Processing Unit ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Lq Control

Abstract This paper presents a vibration control method for piping systems using a feedback control system constructed with LQ-control and a neural network featuring feedback-error learning. The piping system is normally flexible, therefore, natural frequencies of the system fluctuate variably when the density of the content. This paper shows that the piping system changes dynamics according to increases or decreases of the mass effects. In order to reduce the first vibration mode of the piping system without spillover instability, the control system is designed using LQ-control with feedback-error-learning applied to an adapted nonlinear feedback controller. The effectiveness of this control method is confirmed by the neural network simulation program named NeuroLab and is experimented using data measured by the control system constructed with the digital signal processing unit.

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