scholarly journals A temperature predictive control method using BP neural network

2020 ◽  
Vol 782 ◽  
pp. 032040
Author(s):  
Kuan Qian
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Method

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.

Predictive Control of the VAV System Based on Recurrent Wavelet Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 928-933
Author(s):  
Jie Jia Li ◽  
Ben Wang ◽  
Xiao Yan Guo ◽  
Lu Lu Sun
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Method ◽  
Time Lapse ◽  
Wavelet Neural Network ◽  
Control Precision ◽  
Air Volume ◽  
Air Supply ◽  
High Control

An air supply control method of VAV system based on BP neural network is proposed in this paper, which combines with the recurrent wavelet neural network model, predictive control and optimization of parameters. With the proposed method, the air volume of the VAV system can be controlled accurately even if the change of the air is nonlinear and time-lapse. Compared with tradition control method, it has the advantages of rapidly converging, high control precision, strong skills of learning and wide application prospect.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

Design of Vehicle’s Controlling System Used a 4WS Control Method Based on BP Neural Network

2011 ◽  
Vol 201-203 ◽  
pp. 276-280
Author(s):  
Ya Peng Liu ◽  
Yan Tang ◽  
Jia Bin Bi
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Control Method ◽  
Theoretical Method ◽  
Learning Ability ◽  
Controlling System ◽  
Nonlinear Dynamic Characteristics ◽  
Tokyo Institute ◽  
Institute Of Technology ◽  
Technology Institute

In this paper, a 4WS control method based on BP neural network was introduced. It used the BP neural network to simulate the map of vehicle and the nonlinear dynamic characteristics of the tire to avoid large errors that relying on mathematical simulation model of the problem. The 4WS measured data of Tokyo institute of Technology institute of Japan was used and used BP neural network method to identify the nonlinear characteristics of vehicle and tires. System controller’s design is not based on any theoretical method, but on the BP neural network’s self-learning ability. Experimental results show that this method has good controlling characteristics, and it can improve the vehicle’s active safety and manipulating stability effectively.

Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

2013 ◽  
Vol 310 ◽  
pp. 557-559 ◽  
Author(s):  
Li Ji ◽  
Xiao Fei Lian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Tunnel ◽  
Bp Neural Network ◽  
Control Method ◽  
Network Control ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Large Delay ◽  
Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

2013 ◽  
Vol 644 ◽  
pp. 56-59
Author(s):  
Jin Yang Li ◽  
Hong Xia ◽  
Shou Yu Cheng
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

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.

scholarly journals Research on load simulator control strategy based on BP neural network and PID method

2020 ◽  
Vol 306 ◽  
pp. 03002
Author(s):  
Yong Zhou ◽  
Yubo Zhang ◽  
Tianhao Yang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Method ◽  
Vector Method ◽  
Network Learning ◽  
Learning Speed ◽  
Speed Up ◽  
Load Simulator

In the research of load simulator control method, PID control is the most widely used control strategy, but PID controller’s three parameters is difficult to set. This paper proposes a BP neural network feedforward PID controller system which uses BP neural network for setting these parameters, and in order to make the network learning speed up the convergence speed and not fall into local minimum, the adaptive vector method is adopted to improve the algorithm. The simulation and experimental results show that this method is good at avoiding the primeval shock and the sine tracking performance of the system has also been improved.

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.

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