Control System Modeling for Automotive Brake Test-Bench Based on Neural Network

2011 ◽  
pp. 125-130
Author(s):  
Jiaxi Du ◽  
Hong Shen ◽  
Xin Ning
Keyword(s):  
Neural Network ◽  
Control System ◽  
Test Bench ◽  
System Modeling ◽  
Automotive Brake

Control System Modeling for Automotive Brake Test-Bench Based on Improved PID Algorithm

2012 ◽  
Vol 542-543 ◽  
pp. 1007-1010
Author(s):  
Jia Xi Du ◽  
Hong Shen ◽  
Yuexia Feng
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Pid Control ◽  
Test Bench ◽  
Control Function ◽  
Fitness Function ◽  
System Modeling ◽  
Loop Model ◽  
Deviation Function ◽  
Automotive Brake

Based on PID controller, the closed-loop model of instantaneous torque for automotive brake test-bench was established, and then the instantaneous torque and the deviation function were calculated, furthermore, the genetic algorithm was adopted in order to get a global optimal solution and to optimize the parameters of the PID control system. So that the deviation function can be reduced or improved. By comparing the effect of control function, the causes for error volatility of the PID control system were derived, and the fitness function of genetic algorithm was determined reasonably. This algorithm can improve the reliability and accuracy of the control model effectively, and provide an effective method for testing the merits and integrated performance of automotive brake design.

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 the Application of Wavelet Neural Network in Temperature Control System

2014 ◽  
Vol 998-999 ◽  
pp. 642-645
Author(s):  
Gu Yong
Keyword(s):  
Neural Network ◽  
Control System ◽  
Internal Model ◽  
Learning Algorithm ◽  
System Modeling ◽  
Response Speed ◽  
Internal Model Control ◽  
Temperature Control System ◽  
Wide Range ◽  
Model Control

A rapid learning algorithm was put forward to realize complex system modeling and self-adaptive control with uncertainty, high nonlinear and lame time-delay. Merits of internal model control were combined, such as simple design, food regulation capacity, high robustness and the ability to eliminate the unknown disturbance to construct a internal model control system based on wavelets neural network, which was characterized by high robustness and quick response speed and then it can brim food control performance when controlled objects vary in a wide range. Finally, it was triumphantly used in simulation of the superheated steam temperature reduction control system of 500 MW unit and food performances are obtained.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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