The Nonlinear PID Control for Beam Magnetic Suspension System of Gantry-Moving Type Numerically Controlled Machine Tool

2012 ◽  
Vol 546-547 ◽  
pp. 992-996
Author(s):  
Chun Fang Liu ◽  
Bin Zang ◽  
Tong Wang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Magnetic Levitation ◽  
Response Speed ◽  
Fast Response ◽  
Magnetic Suspension ◽  
Control Effect ◽  
Maglev System ◽  
Nonlinear Tracking

The maglev system is a typical nonlinear system, it is difficult to get the best control effect only by nonlinear control method. At first, the maglev system is linearized in this paper, for the classic PID control magnetic levitation system which exists the contradiction between fast and overshoot .This paper adopts the nonlinear tracking-differentiator-based PID controller to control the maglev system. Finally simulation results show that the nonlinear PID controller has fast response speed, no overshoot, and strong robustness in controlling the maglev system.

scholarly journals Research of RBF-PID Control in Maglev System

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1780
Author(s):  
Danrui Ma ◽  
Mengxiao Song ◽  
Peichang Yu ◽  
Jie Li
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Environmental Changes ◽  
Control Method ◽  
Working Environment ◽  
Actual Process ◽  
Control Effect ◽  
Maglev Train ◽  
Rbf Network ◽  
Maglev System

Control of the maglev system is one of the most significant technologies of the maglev train. The common proportion integration differentiation (PID) method, which has fixed control parameters, ignores the non-linearity and uncertainty of the model in the design process. In the actual process, due to environmental changes and interference, the inherent parameters of the system will drift significantly. The traditional PID controller has difficulty meeting the control requirements, and will have poor control effect in the actual working environment. Therefore, a radial basis function (RBF)-PID controller is designed in this article, which can use the information from the levitation system identified by the RBF network to adjust the parameters of the controller in real time. Compared with the traditional PID control method, it is shown that the RBF-PID method can improve the control performance of the system through simulation and experiment.

PID Control of Maglev Levitation System Based on Disturbance Observer

2014 ◽  
Vol 519-520 ◽  
pp. 1353-1359
Author(s):  
Zhao Yu Guo ◽  
Jie Li
Keyword(s):  
Pid Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Parametric Uncertainty ◽  
Response Speed ◽  
Hysteretic System ◽  
Maglev System ◽  
Stability Robustness ◽  
Levitation System ◽  
Speed Stability

The maglev suspension system is a typical nonlinear hysteretic system, which has characteristics of essential unstability, nonlinearity and parametric uncertainty. In this paper, a disturbance observer is designed on the basis of conventional PID control model of maglev system. Simulation results denote that: this control method has more advantages in anti-disturbance ability, response speed, stability, robustness, dynamic and static performances compared with the conventional PID control.

Study on Genetic Neural Network Control Strategy for DC/DC Converter

2011 ◽  
Vol 268-270 ◽  
pp. 1921-1927
Author(s):  
Xi Xiang Zhou ◽  
Jia Sheng Li ◽  
Dan Guo
Keyword(s):  
Neural Network ◽  
Output Voltage ◽  
Control Method ◽  
Response Speed ◽  
Weight Coefficient ◽  
Fast Response ◽  
Network Control ◽  
Initial Weight ◽  
Control Effect ◽  
Genetic Neural Network

A control method based on genetic neural network is presented to deal with the nonlinear object of the high-power PS-FB-ZVS PWM DC/DC converter. The control system optimizes the initial weight of the BP neural network and PID parameters tuning on line utilizing the genetic algorithm, which directly controls the object in closed-loop and has solved the problem that the controller network initial weight coefficient influences the control effect, thus, the optimal dynamic and steady state performance of the system is ensured. In the MATLAB environment, the control systems consisting of different controllers are simulated, and the output voltage and output current waveforms are obtained when the system is loaded by experiment. The results show that the controller has strong robustness, fast response speed, and small output voltage fluctuations with load changes.

Fractional Order PID Control of Railway Rescue Crane Leveling System

2014 ◽  
Vol 620 ◽  
pp. 449-455
Author(s):  
Jun Peng Shao ◽  
Ling Zhang ◽  
Zhao Hui Jin ◽  
Xiao Dong Yang
Keyword(s):  
Fractional Order ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Servo System ◽  
Response Speed ◽  
Simulation Software ◽  
Step Response ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

Aiming at the overshoot problem of electro-hydraulic position servo system used in the process of railway rescue crane hydraulic automatic leveling, and based on its characteristics such as big output power, high control accuracy, quick response speed, et al, this paper established the linear mathematical model of railway rescue crane electro-hydraulic automatic leveling system, besides, the fractional order PID control method was proposed based on the oustaloup digital filter algorithm and the working principle of fractional order PID control method was given, and then the fractional order PID controller was designed. In order to verify the effectiveness of the proposed control strategy, the MATLAB/Simulink simulation software was used. The simulation results show that the fractional order PID controller improved the speed of electro-hydraulic servo system unit step response and the capacity of resisting disturbance compared to traditional integer order PID controller, besides it can ensure the response without overshoot at the same time, so it meets the requirements for rapidity and no overshoot of railway rescue crane hydraulic automatic leveling system very well.

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.

Simulation Research on Fuzzy Adaptive PID Control of Variable Pump Control System

2012 ◽  
Vol 220-223 ◽  
pp. 880-883
Author(s):  
Hui Wang ◽  
Zhuo Xu
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Response Speed ◽  
System Response ◽  
Fuzzy Pid Control ◽  
Simulation Research ◽  
Output Pressure ◽  
System Output ◽  
Fuzzy Adaptive

According to the problem of large overshoot in the variable pump constant pressure output, the fuzzy controller and PID controller were combined. The dynamic response of system output pressure was obtained by combining simulation with a fuzzy adaptive PID controller designed in Matlab/Simulink and mechanical hydraulic model established in AMESim. The simulation results show that fuzzy PID control can achieve the goal of system response without overshoot, and response speed is improved further. The anti-interference ability is also stronger.

Design and Simulation of DC Speed Regulating System Based on Fuzzy Control

2012 ◽  
Vol 220-223 ◽  
pp. 1258-1261
Author(s):  
Li Hong Wang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Dynamic State ◽  
State Function ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
On Line ◽  
Start Process ◽  
The Ideal

PID control is adopted in traditional DC speed regulating system. In start process the current super adjustment value is big. When adding load perturbation and voltage perturbation suddenly, its dynamic state function will be decended. Aimming at this problem, a kind of improved system was put forward. The speed modulator used fuzzy PID controller, according to e and ec, the parameters of the modulator can be modified on line. The current modulator adopted integral separable PID control method. The simulation results indicated that the improved system has better dynamic state function and anti- Rao function. Particularly the start current wave closes to the ideal rectangle wave more. So the responding speed of the system can be sped.

Smith Prediction Based on Genetic Algorithm Self-Tuning PID Control

2014 ◽  
Vol 602-605 ◽  
pp. 1186-1189
Author(s):  
Dong Sheng Wu ◽  
Qing Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Delay System ◽  
Time Delay System ◽  
Self Tuning ◽  
On Line ◽  
Turning Control

Aiming at the phenomena of big time delay are normally existing in industry control, this paper proposes an intelligent GA-Smith-PID control method based on genetic algorithm and Smith predictive compensation algorithm and traditional PID controller. This method uses the ability of on line-study, a self-turning control strategy of GA, and better control of Smith predictive compensation to deal with the big time delay. This method overcomes the limitation of traditional PID control effectively, and improves the system’s robustness and self-adaptability, and gets satisfactory control to deal with the big time delay system.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

2010 ◽  
Vol 139-141 ◽  
pp. 1945-1949
Author(s):  
Tian Pei Zhou ◽  
Wen Fang Huang
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Control Method ◽  
Optimization Method ◽  
Separation Process ◽  
Control Effect ◽  
Environment Change ◽  
Good Convergence ◽  
Single Neuron Pid

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

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