Research on the Application of PID Control with Neural Network and Parameter Adjustment Method of PID Controller

2018 ◽  
Author(s):  
Jiayu Liu ◽  
Wei Pan ◽  
RuoPeng Qu ◽  
Meng Xu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Parameter Adjustment ◽  
Adjustment Method

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.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Research on Control Strategy of Automatic Control System for Sewage Treatment

2013 ◽  
Vol 791-793 ◽  
pp. 690-693
Author(s):  
Zhang Hong ◽  
Xiao Liang Liu ◽  
Fang Wei
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Bp Algorithm ◽  
Dissolved Oxygen Concentration ◽  
Control Effect

For the characteristics of the sewage treatment process and a combination of BP algorithm and conventional PID control, a PID controller is proposed based on BP neural network to realize the online adjustment of PID controller parameters. This control strategy will be applied to the control of the DO(Dissolved Oxygen) concentration in sewage treatment, and a contrast has been made with conventional PID control effect.

WNN Optimizing PID Controller for BLDC Control System

2013 ◽  
Vol 278-280 ◽  
pp. 1529-1532
Author(s):  
Hong Pei Han ◽  
Wu Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
High Efficiency ◽  
Good Control ◽  
Industrial Applications ◽  
Wavelet Neural Network ◽  
Control Effect ◽  
Pid Tuning

Brushless DC motors (BLDC) are widely used for many industrial applications because of their high efficiency, high torque and low volume. This paper presents the PID control for BLDC Motor, because good control effect cannot be acquired by using the traditional PID control in the non-linear variable time servomechanism and it is difficult to tune the parameters and get satisfied control characteristics, some intelligent techniques should be taken. Wavelet Neural Network (WNN) was constrictive and fluctuant of wavelet transform and has self-study, self adjustment and nonlinear mapping functions of neural networks, So, a wavelet neural network self-tuning proportional-integral-derivative (PID) controller was proposed. The structure of WNN and PID tuning with WNN was presented and the equivalent circuit of BLDC and its mathematical models was analyzed, the simulation was taken with new method, the efficiency and advantages of this control strategy was successfully demonstrated which can applied into BLDC control system.

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.

scholarly journals Ship RBF neural network sliding mode PID heading control

2022 ◽  
Vol 355 ◽  
pp. 03064
Author(s):  
Jiaming Yu ◽  
Renxiang Bu ◽  
Liangqi Li
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Variable Structure ◽  
Control Parameters ◽  
Mmg Model ◽  
Heading Control ◽  
Online Tuning

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

scholarly journals Design of a New Catheter Operating System for the Surgical Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xu Ma ◽  
Jinpeng Zhou ◽  
Xu Zhang ◽  
Yang Qi ◽  
Xiaochen Huang
Keyword(s):  
Neural Network ◽  
Operating System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Radiation Injury ◽  
Control Parameters ◽  
Simulation Experiments ◽  
Neural Network Pid

In interventional surgery, the manual operation of the catheter is not accurate. It is necessary to operate the catheter skillfully and effectively to protect the surgeon from radiation injury. The purpose of this paper is to design a new robot catheter operating system, which can help surgeons to complete the operation with high mechanical precision. On the basis of the original mechanical structure—real catheter, the operation information of the main end operator is collected. After the information is collected, the control algorithm of the system is improved, and the BP neural network is combined with the traditional PID controller to adjust the PID control parameters more effectively and intelligently so that the motor can reflect the output of the controller better and faster. The feasibility and superiority of the BP neural network PID controller are verified by simulation experiments.

Research on Load Simulator Based on CMAC Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 2169-2172 ◽  
Author(s):  
Ju Tian ◽  
Yan Ying Guo
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Feed Forward Control ◽  
Cmac Neural Network ◽  
On Line ◽  
Parallel Control ◽  
Load Simulator ◽  
Simulation Results ◽  
On Line Learning

A novel parallel control strategy based on the CMAC controller and PID control is proposed which is to solve the problem of extraneous torque which exists in the load simulator. In which, the CMAC controller realizes feed forward control, yet conventional PID controller only realizes feedback control, and using the PD algorithm improves system. The simulation results show that output of the conventional PID controller completes unceasing on-line learning by the CMAC neural network. Effectively suppress the extra torque of the load simulator, the Load Simulator system performance has been improved significantly.

scholarly journals A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhekang Dong ◽  
Shukai Duan ◽  
Xiaofang Hu ◽  
Lidan Wang ◽  
Hai Li
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Small World ◽  
Activation Function ◽  
Simulink Model ◽  
Implementation Scheme ◽  
Intelligent Pid ◽  
Speed And Accuracy ◽  
Computation Speed

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

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