Application of CMAC Neural Network & PID Control on Hydraulic Speed Servo System

2011 ◽  
Vol 323 ◽  
pp. 128-132
Author(s):  
Rui Tang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Servo System ◽  
Cerebellar Model Articulation Controller ◽  
Cmac Neural Network ◽  
Control Scheme ◽  
Tracking Ability ◽  
Velocity Tracking ◽  
The Mathematical Model ◽  
Neural Network Pid

Pointing on nonlinear and parameters vary with time in the velocity servo system, and the requirement of servo system is difficult to meet by the traditional PID control scheme. A control scheme of ANN-based PID controller is developed here for velocity tracking control for an electro-hydraulic velocity servo system. In this paper the mathematical model is established for the system, and the PID controller based on cerebellar model articulation controller (CMAC) are designed and connected in parallel. Numerical simulation results indicate that the proposed control scheme has an excellent performance on high precision velocity tracking ability and rejection of disturbance, comparing with conventional PID control strategy.

scholarly journals Optimization of electrochemical parameters in microbial fuel cell system based on Fuzzy-PID and CMAC neural network

2019 ◽  
Vol 9 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Chengcai Fu ◽  
Fengying Ma
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Microbial Fuel Cells ◽  
Pid Controller ◽  
Cerebellar Model Articulation Controller ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Cmac Neural Network ◽  
Electrochemical Parameters ◽  
Control Scheme

Due to the extensive application prospects on wastewater treatment and new energy development, microbial fuel cells (MFCs) have gained more and more attention by many scholars all over the world. The bioelectrochemical reaction in MFC system is highly complex, serious nonlinear and time-delay dynamic process, in which the optimal control of electrochemical parameters is still a considerable challenge. A new optimal control scheme for MFC system which combines proportional integral derivative (PID) controller with parameters fuzzy optional algorithm and cerebellar model articulation controller (CMAC) neural network was proposed. The simulation results demonstrate that the proposed control scheme has rapider response, better control effect and stronger anti-interference ability than Fuzzy PID controller by taking constant voltage output of MFC under the different load disturbances as example.

The Simulation of Robot Control Based on CMAC Neural Network PID Control Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 659-663
Author(s):  
Jian Li Yu ◽  
Ya Zhou Di ◽  
Lei Yin
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Robot Control ◽  
Control Algorithm ◽  
External Disturbance ◽  
System Model ◽  
Robot System ◽  
System Parameters ◽  
Cmac Neural Network ◽  
Neural Network Pid

According to the problem of nonlinear and uncertainty in robot control, this paper proposes a PID control algorithm based on CMAC neural network model, for the elimination of the influence of uncertainty caused by robot system parameters and external disturbance. The simulation results show that this algorithm can effectively overcome the uncertainties and external disturbance of robot system model, this algorithm has good robustness and stability, its performance is superior to the traditional PID control algorithm.

Analysis of Speed Servo System of Pneumatic Manipulator Based RBF Neural Network PID Control

2010 ◽  
Vol 40-41 ◽  
pp. 65-70 ◽  
Author(s):  
Jing Luo ◽  
Rui Bo Yuan ◽  
Yu Bi Yuan ◽  
Shao Nan Ba ◽  
Zong Cheng Zhang
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Hybrid Control ◽  
Rbf Neural Network ◽  
Servo System ◽  
Fast Response ◽  
Quick Response ◽  
Pneumatic Servo System ◽  
The Stability ◽  
Neural Network Pid

Through analysis and comparison of simple PID control and RBF neural network-PID hybrid control of the pneumatic servo system, then compared the stability and quick response under the two control system. Concluded that RBF neural network-PID hybrid control has better stability and fast response than the simple PID control.

Application of Compound PID Control in the DC Servo Motor

2009 ◽  
Vol 16-19 ◽  
pp. 145-149 ◽  
Author(s):  
Xiao Yan Song ◽  
Qing Jie Yang ◽  
Xue Ming Zhang ◽  
Qi Gao Feng
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Pid Control ◽  
Pid Controller ◽  
Design Method ◽  
Servo System ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Fuzzy Pid Controller ◽  
Performance Requirements

Although the traditional PID controller is widely used in many fields, the system parameters varying and external disturbances existing in the DC servo system will cause large overshoot or poor stability. To improve the performance of the PID controller, a compound servo control system combining the conventional PID control and the fuzzy control is presented to meet the demand of a vehicular antenna servo system in this paper. Incorporating the fuzzy control and the conventional PID control, this paper presents a design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas. Simulation results are presented to show the efficiency of the proposed controller. The practical control effect shows that the control system that adopts the fuzzy PID controller has better performance than that of the traditional PID control system, and meets the performance requirements of the servo system.

Anti-Swing Control of Container Crane Based on CMAC-PID Controller

2012 ◽  
Vol 503-504 ◽  
pp. 1256-1259
Author(s):  
Ya Li Su ◽  
Xi Huai Wang ◽  
Jian Mei Xiao
Keyword(s):  
Mathematical Model ◽  
Pid Control ◽  
Pid Controller ◽  
Container Crane ◽  
Compound Control ◽  
Simulation Results ◽  
Control Principle ◽  
The Mathematical Model

This paper establishes the mathematical model of the system of container crane, and then introduced the CMAC and PID compound control principle and characteristics, and PID control of the position of the container crane , CMAC-PID control of the position of the container crane, and CMAC-PID control of container crane's position and angle. The simulation results show that the crane based on the CMAC and PID compound control has a very good positioning and anti-swing effect.

Modeling and Simulation Research of NC Machine Servo System Based on Fuzzy PID Control

2012 ◽  
Vol 214 ◽  
pp. 924-928
Author(s):  
Mao Yao Ao
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Servo System ◽  
Response Speed ◽  
Quick Response ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Simulation Research ◽  
Fuzzy Pid Controller ◽  
Nc Machine

Traditional PID control has not been able to meet the control requirements. This paper has designed one type of Fuzzy PID controller to use in NC Machine Servo System. Simulate the response of Fuzzy PID controller and traditional PID controller by Simulink in MATLAB. The simulation results indicated that the Fuzzy PID controller had quick response speed, strong robustness, high precision and non-overshoot etc. It improved the whole control performance of NC Machine Servo System.

PID Control Algorithm Based on Genetic Algorithm and its Application in Electric Cylinder Control

2017 ◽  
Vol 12 (3) ◽  
pp. 51-61 ◽  
Author(s):  
Geng Zhang ◽  
Xiansheng Gong ◽  
Xirui Chen
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Parameter Tuning ◽  
Search Space ◽  
Control Field ◽  
Electric Cylinder ◽  
Complex Phenomena ◽  
The Mathematical Model

The traditional PID controller is simple in principle, easy to use, stable and reliable, and it is still widely used in the control field. However, for many nonlinear and lagging objects, the parameter tuning of PID controller is very important. Genetic algorithm provides a new way to optimize the parameters of PID. It uses simple coding techniques and propagation mechanisms to express complex phenomena, which is not restricted by the restriction of the search space. In this paper, the global optimization of genetic algorithm is used to optimize the parameters of PID, which can improve the performance and adaptive capability of PID controller. The mathematical model of the electric cylinder system is established, and the PID controller based on genetic algorithm is used to control the system. The simulation results verify the effectiveness of the proposed control algorithm.

Research of a Robust Control for Electro Hydraulic Servo System

2014 ◽  
Vol 986-987 ◽  
pp. 1103-1107
Author(s):  
Xiao Gang Tang ◽  
Chao Zhang
Keyword(s):  
Robust Control ◽  
Pid Control ◽  
Pid Controller ◽  
Manufacturing Industry ◽  
The Novel ◽  
Servo Systems ◽  
Novel Approach ◽  
Aerospace Manufacturing ◽  
Control Scheme ◽  
Hydraulic Servo

In recent years, electro hydraulic servo systems are more and more widely used in the aerospace, manufacturing industry and the agricultural machinery. As a result, higher requirements are put forward for the performance and application environment, including better tracking precision and respond speed. Aiming at the shortcomings of conventional PID controller, such as the large overshoot, long transfer time and poor robustness performance, a robust control scheme with dual-loop structure is proposed in this paper. Simulation results show that better performances are acquired in the novel approach in contrast with the PID control scheme and the structure of the proposed scheme is simple and easy to implement.

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.

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