Simulation of PID Control of Belt Conveyor System in Coal Mine by an Improved Adaptive Genetic Algorithm

2014 ◽  
Vol 614 ◽  
pp. 215-218 ◽  
Author(s):  
Lei Yu ◽  
Xu Long Zhang ◽  
Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Coal Mine ◽  
Pid Control ◽  
Pid Controller ◽  
Parameters Optimization ◽  
Step Response ◽  
Adaptive Genetic Algorithm ◽  
Belt Conveyor ◽  
Conveyor System ◽  
And Performance

In order to improve the problem of premature and performance of optimization, an improved adaptive genetic algorithm is proposed for parameters optimization of coal mine belt conveyor PID controller by applying the number of iterations to the crossover operation and mutation operation of genetic algorithm. The simulation shows that the step response of the improved algorithm is superior to the traditional adaptive genetic algorithm.

Optimization of the General Aviation Aircraft Longitudinal Controller Parameters Based on Genetic Algorithm

2011 ◽  
Vol 361-363 ◽  
pp. 1795-1798
Author(s):  
Yang Cao ◽  
Zhan Shuang Hu ◽  
Wei Ping Zhao
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Pid Controller ◽  
Channel Model ◽  
Control Method ◽  
General Aviation ◽  
Parameters Optimization ◽  
System Control ◽  
Control Parameters ◽  
General Aviation Aircraft

The PID control is a popularly control method that used in control systems, which is still adopted in many practical application processes.This paper is based on the oringinal navigation aircraft longitudinal channels model,and discusses the setting of navigation autopilot longitudinal channels model in the PID controller parameters, using General Aviation Aircraft Longitudinal channels of PID control parameters optimization based on Genetic Algorithm.In contrast with the simulink7.1 simulation results of the longitudinal channel model, base on the thought of genetic algorithm and use MATLAB language program to get the parameters of the controller, adjusting the parameters to achieve the optimization of PID control parameters. The result of SIMULINK simulation: The PID controller which is designed by using the genetic algorithm is more adaptability, flexibility, and can guarantee the system control effect.

Application of Control Parameters Optimization of CNC Servo System Based on Self-Adaptive Genetic Algorithm

2011 ◽  
Vol 239-242 ◽  
pp. 2847-2850
Author(s):  
Gui Rong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Population Diversity ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Fitness Value ◽  
Current Engineering ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Self Adaptive

In order to solve the shortcomings of current engineering methods for parameters adjustment that can only deal with them according to single requirement of system and can not optimize them in the whole range, as well as the standard genetic algorithm is prone to premature convergence, therefore, an improved PID parameters adjustment method based on self-adaptive genetic algorithm was proposed. This approach enables crossover and mutation probability automatically change along with the fitness value, not only can it maintain the population diversity, but also can ensure the convergence of the algorithm. A comparison of the dynamic response between the traditional PID control and the PID control based on self-adaptive genetic algorithm was made. Simulation results show that the latter has much superiority.

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.

Adaptive Genetic Algorithm Based Parameters Optimization of PI Controller for Battery System in Wind Farm

2014 ◽  
Vol 1055 ◽  
pp. 375-382
Author(s):  
Hui Ren ◽  
Dan Wei ◽  
David Watts ◽  
Jia Qi Fan
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Storage System ◽  
Pi Controller ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Trial And Error ◽  
Battery System ◽  
Trial And Error Method ◽  
Error Method

The randomness and intermittence of wind farm real power generation bring challenges to power system operation, and installing battery system for the mitigation of the fluctuation of wind farm output, following the short-term forecasting curve, even adjusting the output according to the operator’s requirement is a possible way to address the problem from the wind farm side. After a review of various storage control strategies for stabilizing the fluctuation of wind power output, the model of battery energy storage system as well as its control strategy is introduced. Adaptive Genetic Algorithm (AGA) is used for the optimization of PI control parameters. Simulation shows the effectiveness of the proposed method. Moreover, comparing with the trial-and-error method, the optimization algorithm proposed has the advantage of finding the optimal parameters under the lack of experience on PID control, and combined with trial-and-error method, the difficulties engineer could face on tuning the parameters of PI controller is decreased, which increases the feasibility for parameters of PI controller’s being transplanted to similar applications.

Optimal fuzzy PID controller design for an active magnetic bearing system based on adaptive genetic algorithms

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
HUNG-CHENG CHEN
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Adaptive Genetic Algorithm ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Pid Controller Design ◽  
Amb System

We propose an adaptive genetic algorithm (AGA) for the multi-objective optimisation design of a fuzzy PID controller and apply it to the control of an active magnetic bearing (AMB) system. Unlike PID controllers with fixed gains, a fuzzy PID controller is expressed in terms of fuzzy rules whose consequences employ analytical PID expressions. The PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than conventional ones. Moreover, it can be easily used to develop a precise and fast control algorithm in an optimal design. An adaptive genetic algorithm is proposed to design the fuzzy PID controller. The centres of the triangular membership functions and the PID gains for all fuzzy control rules are selected as parameters to be determined. We also present a dynamic model of an AMB system for axial motion. The simulation results of this AMB system show that a fuzzy PID controller designed using the proposed AGA has good performance.

Nonlinear Immune PID Controller and its Application to the Heat Milling System’s Material-Level Control

2011 ◽  
Vol 383-390 ◽  
pp. 743-749
Author(s):  
Jiu Qing Liu ◽  
Wei Wang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Feedback Mechanism ◽  
Control Technique ◽  
Level Control ◽  
Stability Robustness ◽  
Nonlinear Pid Controller ◽  
Simulation Results ◽  
And Performance ◽  
The Stability

Based on the fusion of immune feedback mechanism for the conventional PID control technique, a new immune nonlinear PID controller is proposed in this paper. The stability of immune nonlinear PID is analysised using Popov stability criterion. The controller designed not only guarantees the stability robustness and performance robustness of the system but also the tracking performance of the system. The numerical simulation results of the Material-level control of the heat milling system show the effectiveness and feasibility of our immune unlinear PID are verified in Mat lab.

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