scholarly journals Research on Boiler Water Supply Control System Based on AT89C55 and Fractional order PID Algorithm

2019 ◽  
Vol 154 ◽  
pp. 173-180
Author(s):  
Jun-Li Gao
Keyword(s):  
Control System ◽  
Water Supply ◽  
Fractional Order ◽  
Boiler Water ◽  
Pid Algorithm ◽  
Fractional Order Pid

Fuzzy Fractional Order PID Based Parallel Cascade Control System

2014 ◽  
pp. 293-302 ◽  
Author(s):  
Rangaswamy Karthikeyan ◽  
Sreekanth Pasam ◽  
Sandu Sudheer ◽  
Vallabhaneni Teja ◽  
Shikha Tripathi
Keyword(s):  
Control System ◽  
Fractional Order ◽  
Cascade Control ◽  
Fractional Order Pid ◽  
Cascade Control System

scholarly journals Improvement of the Control System Performance based on Fractional-Order PID Controllers and Models with Robustness Considerations

2018 ◽  
Vol 51 (4) ◽  
pp. 551-556 ◽  
Author(s):  
H. Meneses ◽  
E. Guevara ◽  
O. Arrieta ◽  
F. Padula ◽  
R. Vilanova ◽  
...  
Keyword(s):  
Control System ◽  
Fractional Order ◽  
System Performance ◽  
Pid Controllers ◽  
Fractional Order Pid

scholarly journals Magnetorheological Elastomer Precision Platform Control Using OFFO-PID Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying-Qing Guo ◽  
Jie Zhang ◽  
Dong-Qing He ◽  
Jin-Bao Li
Keyword(s):  
Real Time ◽  
Fractional Order ◽  
Pid Controller ◽  
Vibration Isolation ◽  
Dynamic Responses ◽  
Magnetorheological Elastomer ◽  
Controlled System ◽  
Pid Algorithm ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

The magnetorheological elastomer (MRE) is a kind of smart material, which is often processed as vibration isolation and mitigation devices to realize the vibration control of the controlled system. The key to the effective isolation of vibration and shock absorption is how to accurately and in real time determine the magnitude of the applied magnetic field according to the motion state of the controlled system. In this paper, an optimal fuzzy fractional-order PID (OFFO-PID) algorithm is proposed to realize the vibration isolation and mitigation control of the precision platform with MRE devices. In the algorithm, the particle swarm optimization algorithm is used to optimize initial values of the fractional-order PID controller, and the fuzzy algorithm is used to update parameters of the fractional-order PID controller in real time, and the fractional-order PID controller is used to produce the control currents of the MRE devices. Numerical analysis for a platform with the MRE device is carried out to validate the effectiveness of the algorithm. Results show that the OFFO-PID algorithm can effectively reduce the dynamic responses of the precision platform system. Also, compared with the fuzzy fractional-order PID algorithm and the traditional PID algorithm, the OFFO-PID algorithm is better.

scholarly journals Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Gao ◽  
Jilin Chen ◽  
Li Wang ◽  
Shiqing Xu ◽  
Yuanlong Hou
Keyword(s):  
Control System ◽  
Multiobjective Optimization ◽  
Fractional Order ◽  
Optimization Design ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Gun Control ◽  
Tuning Method ◽  
Fractional Order Pid

Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

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