The Design of Expert PID Parameter Optimized by Genetic Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 3091-3094
Author(s):  
Jia Tang Cheng ◽  
Wei Xiong ◽  
Li Ai
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Parameter Tuning ◽  
Expert Pid ◽  
Parameter Values ◽  
Better Than

Aiming at the problems Expert PID parameter tuning for time-consuming, and the results are not necessarily the best. In this paper, genetic algorithm is introduced to the parameter optimization, finally get a set of optimal PID parameter values. In comparison with simulated experiments, the results show that the performance of the Designed to optimize the performance of optimization expert PID controller is better than conventional controller, can achieve good dynamic performance.

The Optimization of the PID Controller Using the Least Squares Method and the Improved Lbest PSO Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1296-1303 ◽  
Author(s):  
Chuan Gui Yang ◽  
Zhao Jun Yang ◽  
Fei Chen ◽  
Yan Zhu ◽  
Ying Nan Kan ◽  
...  
Keyword(s):  
Least Squares ◽  
Pid Controller ◽  
Least Squares Method ◽  
Dynamic Performance ◽  
Pso Algorithm ◽  
Pid Tuning ◽  
Loading System ◽  
Hydraulic Servo ◽  
Tuning Scheme ◽  
Better Than

A self-adaptive PID tuning scheme is presented for the electro-hydraulic servo loading system. It requires the least squares method to identify the parameters of the transfer function of the electro-hydraulic servo loading system and utilizes the improved lbest PSO algorithm to optimize the PID controller. The scheme can provide the optimal PID parameters so that the dynamic performance and stability of the electro-hydraulic servo loading system are improved. Results show the fact that the dynamic performance and stability of the system are improved by the scheme. And in terms of optimization of PID controller, the improved lbest PSO algorithm is better than the lbest PSO algorithm and Ziegler-Nichols method.

scholarly journals Attitude Active Disturbance Rejection Control of the Quadrotor and Its Parameter Tuning

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Suiyuan Shen ◽  
Jinfa Xu
Keyword(s):  
Flight Control ◽  
Disturbance Rejection ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
External Disturbance ◽  
Parameter Tuning ◽  
Adaptive Genetic Algorithm ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Better Than

The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simulations revealed that the dynamic performance and robustness of the ADRC is better than that of the SMC.

Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor

2011 ◽  
Vol 130-134 ◽  
pp. 1938-1942
Author(s):  
Xia Bo Shi ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Motor Speed ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ipso Algorithm

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

FPGA-Based Expert PID Controller for Buck DC-DC Converter

2013 ◽  
Vol 431 ◽  
pp. 215-220 ◽  
Author(s):  
Yao Chen ◽  
Chang Yuan Chang ◽  
Yan Yan
Keyword(s):  
Pid Controller ◽  
Stable Steady State ◽  
Buck Converters ◽  
Steady State Response ◽  
Control Laws ◽  
State Response ◽  
Digitally Controlled ◽  
Expert Theory ◽  
Expert Pid ◽  
Better Than

In this paper, an expert PID controller used for digitally controlled DC-DC buck converters is proposed. It is designed bycombing expert theory with traditional PID theory, basing on the controlled objects and the knowledge of control laws. The controller uses the expert experiences to regulate the PID parameters online according to the dynamic characteristics of the system. The simulation and practical results indicated that, comparing with conventional PID controller, the expert PID controller which has faster startup transient response, smaller overshoot and more stable steady-state response, is better than conventional PID controller. It is well fit for the demand of control system.

Research of Speed Regulating System Based on Expert PID Control

2014 ◽  
Vol 526 ◽  
pp. 257-262
Author(s):  
Hui Wang ◽  
Hong Xia Liu
Keyword(s):  
Expert System ◽  
Permanent Magnet ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Synchronous Motor ◽  
Motor Speed ◽  
System Technology ◽  
Pid Algorithm ◽  
Linear Synchronous Motor ◽  
Expert Pid

In order to further improve on the static and dynamic performance of the permanent magnet linear synchronous motor speed regulating system, the traditional PID controller is combined with the expert system technology to achieve optimum control of the control systems. In light of defects of the traditional PID controller, the expert system is introduced into the control system of motor on the basis of the incremental PID algorithm, and it is applied to the permanent magnet linear synchronous motor to adjust the motors speed. The simulation results prove the new PID controller combined with the expert system technology has small overshoot, rapid response and good robust stability.

scholarly journals Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

2021 ◽  
Author(s):  
Nigel P. A. Browne
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Genome Size ◽  
Parameter Optimization ◽  
Gene Expression Programming ◽  
Parameter Tuning ◽  
Trial And Error ◽  
New Mutation ◽  
Novel Method ◽  
Linear Chromosomes

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a novel method for adaptively tuning the genome size is presented. The approach introduces new mutation, transposition and recolI)bination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations and enhances parallel GEP. To test our approach an assortment of problems were used, including symbolic regression, classification and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptively tuning the genome size of GEP's representation.

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.

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