Coal Belt Conveyor PID Controller Parameter Regulation with Neural Network

2013 ◽  
Vol 319 ◽  
pp. 583-589 ◽  
Author(s):  
Xian Min Ma ◽  
Xiong Xiong Gao
Keyword(s):  
Neural Network ◽  
Network Theory ◽  
Pid Controller ◽  
Direct Torque Control ◽  
Torque Control ◽  
Belt Conveyor ◽  
Real Time Control ◽  
Time Control ◽  
Working Space ◽  
Neural Network Theory

The coal belt conveyor is an important transport equipment to transfer the coal between the mining working space and the ground. In the traditional belt conveyor control system, there are some disadvantages such as movement quiver with switch relay controller. In this paper, a control strategy using neural network theory to optimize the parameters of the speed PID controller is proposed to overcome the shortcomings, the direct torque control model is adopted to meet real time control requirement, and the optimization steps are described. The theory analysis and simulation results indicate that the system speed overshoot is small after the parameters of the speed PID controller are optimized with neural network theory, so the stability of the coal belt conveyor electrical control driving system is improved.

A Study on PID Control and Simulation Based on BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 742-745
Author(s):  
Fang Fang Zhai ◽  
Shao Li Ma ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Uncertain System ◽  
Real Time Control ◽  
Time Control ◽  
The Neural Network ◽  
Neural Network Pid

This paper introduces the neural network PID control method, in which the parameters of PID controller is adjusted by the use of the self-study ability. And the PID controller can adapt itself actively. The dynamic BP algorithm of the three-layered network realizes the online real-time control, which displays the robustness of the PID control, and the capability of BP neural network to deal with nonlinear and uncertain system. A simulation is made by using of this method. The result of it shows that the neural network PID controller is better than the conventional one, and has higher accuracy and stronger adaptability, which can get the satisfied control result.

Constant Acceleration Control of Valve-Control-Cylinder System Based on Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 2041-2045
Author(s):  
Jun Gong Ma ◽  
Xian Yang Shang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Mathematic Model ◽  
Neural Element ◽  
Real Time Control ◽  
Time Control ◽  
Set Up ◽  
Valve Control ◽  
Control Cylinder

To solve the serious problem of the nonlinear and Time-varying uncertainty of the valve-control-cylinder system, a control system was designed with neural-proportion-integral-differential (PID) theory. Because of the capacity of neural network, the control system showed adaptive capacity in the system of valve-control-cylinder. In this paper, the basic theory of a single neural element self-adaptive PID controller and a model identifier based on Radial Basis Function were described. The mathematic model of the valve-control-cylinder control system was set up. The simulation results prove that the neural-PID system can regulate the PID parameters dynamically by self-learning so that the system with the neural-PID controller showed quick track performance and capacity against the disturbance. The results also prove the validity and applicability of the system. The algorithm is simple, PID initial parameters are easy to adjust, easy in application of the real-time control the valve-control-cylinder system.

scholarly journals Optimized PID Position Control of a Nonlinear System Based on Correlating the Velocity with Position Error

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Nenad Muškinja ◽  
Matej Rižnar
Keyword(s):  
Pid Controller ◽  
Position Control ◽  
Design Approach ◽  
Control Loop ◽  
Real Time Control ◽  
Laboratory Setup ◽  
Time Control ◽  
Beam System ◽  
Ball Velocity ◽  
Ball And Beam System

We examined a design approach for a PID controller for a nonlinear ball and beam system. Main objective of our research was to establish a nonmodel based control system, which would also not be dependent on a specific ball and beam hardware setup. The proposed PID controller setup is based on a cascaded configuration of an inner PID ball velocity control loop and an outer proportional ball position control loop. The effectiveness of the proposed controller setup was first presented in simulation environment in comparison to a hardware dependent PD cascaded controller, along with a more comprehensive study on possible design approach for optimal PID controller parameters in relation to main functionality of the controller setup. Experimental real time control results were then obtained on a laboratory setup of the ball and beam system on which PD cascaded controller could not be applied without parallel system model processing.

A Kind of Environment Monitoring System Used for Aircraft Fire Training Simulator

2013 ◽  
Vol 760-762 ◽  
pp. 1250-1253
Author(s):  
Chun Guo Fei ◽  
Jin Long Zhang ◽  
Tian Hao Liu ◽  
Hai Zhong Xu
Keyword(s):  
Monitoring System ◽  
Pid Controller ◽  
Closed Loop ◽  
Real Time Control ◽  
Training Simulator ◽  
Training Environment ◽  
Time Control ◽  
Sensor Detection ◽  
Training Efficiency ◽  
Command Center

Aircraft fire training simulators are key facilities in airport used for firefighters to do firefighting trainings. In order to protect the safety of firefighters, the monitoring system should be applied to monitor the internal environment of the simulator. In accordance with the requirements of the training environment, a kind of monitoring system based on MCU and GPRS communication components are built. The parameterized PID controller, the sensor detection module, the fan and spray drive module are consisted of closed-loop to achieve real-time control and regulation on the smoke and temperature of the internal simulator. Using GPRS module, the internal scenes of the simulator are sent to the command center through the information transmission system. Based on the information transported from training site, command center can take the appropriate training programs to guide firefighters. Use this system, the training safety is ensured and the training efficiency is improved at the same time.

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