Research on PID Neural Network Control System of Temperature for Agricutural Film Unit

2013 ◽  
Vol 341-342 ◽  
pp. 694-699
Author(s):  
Yue Feng ◽  
Mei Xia Qiao ◽  
Shuai Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Melting Process ◽  
Network Control ◽  
Heating Process ◽  
Neural Network Control ◽  
Proportional Integral ◽  
Pid Neural Network

The temperature of agricultural film unit affects the plastic film directly. Since unit heating process has the characters of time delay, nonlinear, time-varying and strong coupling. It is difficult to create a mathematical model structure of plastic melting process. Thus, temperature control is very difficult. This paper presents decoupling control strategy and corresponding control algorithm based on PID (proportional-Integral-differential) neural network. Proportional, integral, differential neurons form a three-layer neural network. This design gives full play to respective advantages of PID control and neural network, and takes advantage of BP neural network to establish the dynamic model of system.

The Electro-Hydraulic Proportional Control and Simulation of the Radial Piston Pump Based on Proportional-Integral-Derivative Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1749-1752
Author(s):  
Lan Li ◽  
Jiang Ye ◽  
Xue Fei Zheng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Piston Pump ◽  
System Response ◽  
Neural Network Control ◽  
Proportional Control ◽  
Pid Neural Network

In this paper a new control method has been studied in which PID control system was integrated into the neural network. It could overcome some disadvantages such as neural network’s slow rate of convergence and PID’s difficulty in application of multivariate nonlinear systems. A controller of the Electro-hydraulic proportional control stroking mechanism for radial piston pump was designed based on the PID neural network control algorithm. The system responses of system variable control signal of system track were achieved by computer simulation. It was found by PIDNN that the control system could reach steady state in a shorter time, compared with PID control system response time by 65% to 80%. The simulation results showed that the controller for the Electro-hydraulic proportional Radial Piston Pump based PID neural network control algorithm would have a good controlling performance.

scholarly journals Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3069-3077
Author(s):  
Feilong Zheng ◽  
Yundan Lu ◽  
Shuguang Fu
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Temperature Control ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Furnace Temperature ◽  
Proportional Integral ◽  
Intelligent Pid

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

scholarly journals A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hemiao Liu ◽  
Yanming Cheng ◽  
Yulian Zhao ◽  
Mahmoud Al Shurafa ◽  
Jing Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Network Control ◽  
Maximum Efficiency ◽  
Neural Network Control ◽  
Control Performance ◽  
Wide Range ◽  
Neural Network Pid

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

scholarly journals A study of PID Neural Network Control Algorithm for Electric Gas Regulator

2016 ◽  
Vol 9 (11) ◽  
pp. 233-248
Author(s):  
Yuanchang Zhong ◽  
Shaojing Xing ◽  
Guolong Zhao ◽  
Xiaochen Zhang ◽  
Jing Qiao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Network Control ◽  
Neural Network Control ◽  
Pid Neural Network

PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

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