【PID优化】基于正余弦算法 (SCA)优化PID实现微型机器人系统位置控制附simulink模型和matlab代码

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⛄ 内容介绍

本研究提出了一种使用比例-积分-微分 (PID) 控制器控制微型机器人系统位置的新方法。利用正余弦算法（SCA），应用新的目标函数积分平方时间乘以误差平方（ISTES）得到最优PID控制器指标。通过与基于 ISTES 目标函数的灰狼优化 (GWO) 进行比较，验证了所提出的基于 SCA 的控制器的效率。将使用 MATLAB、Simulink 将每种控制技术应用于已识别的模型，并使用 LABVIEW 软件进行实验测试。此外，与基于上升、设置时间和设置误差的 GWO 技术相比，SCA 技术具有最高的性能。因此，建议应用 SSA 来调整 PID 的参数，因为它可以提高其在微型机器人系统中的性能。发现使用 SCA 的错误量比以前的其他实验减少了 37.5%。

⛄ 部分代码

close all

clear all

clc

global Kp Ki Kd

% Opening simulink model

open('PID_controlled_process.slx')

%% problem definition

objfun = @(K)ISTES_PID(K);

N = 3; % NO OF VARIABLES

Kmin = [0 0 0]; % Vector of minimum value of variables

Kmax = [100 1 1]; % Vector of maximum value of variables

SearchAgents_no=30; % Number of search agents

Max_iteration=25; % Maximum numbef of iterations

lb = Kmin;

ub = Kmax;

dim = N;

fobj = objfun;

% Load details of the selected benchmark function

%[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_score,Best_pos,cg_curve]=SCA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

figure('Position',[284 214 660 290])

semilogy(cg_curve,'Color','b')

title('Convergence curve')

xlabel('Iteration');

ylabel('Best flame (score) obtained so far');

axis tight

grid off

box on

legend('SCA')

display(['The best solution obtained by SCA is : ', num2str(Best_pos)]);

⛄ 运行结果

⛄ 参考文献

1- Eissa, M. M., Virk, G. S., AbdelGhany, A. M., & Ghith, E. S. (2013). Optimum induction motor speed control technique using particle swarm optimization. International Journal of Energy Engineering, 3(2), 65-73.‏ 2-

2- Eissa, M. M., Virk, G. S., AbdelGhany, A. M., & Ghith, E. S. (2013). Optimum induction motor speed control technique using genetic algorithm. Am. J. Intell. Syst, 3(1), 1-12.‏

3- Ghith, E. S., & Tolba, F. A. A. (2022, March). Real-Time Implementation of Tuning PID Controller Based on Whale Optimization Algorithm for Micro-robotics System. In 2022 14th International Conference on Computer and Automation Engineering (ICCAE) (pp. 103-109). IEEE.‏

4- Ghith, E. S., & Tolba, F. A. A. (2022). Design and Optimization of PID Controller using Various Algorithms for Micro-Robotics System. Journal of Robotics and Control (JRC), 3(3), 244-256.‏

5- Ghith, E. S., & Tolba, F. A. A. (2022). LabVIEW Implementation of Tuning PID Controller Using Advanced Control Optimization Techniques for Micro-robotics System. International Journal of Mechanical Engineering and Robotics Research, 11(9).‏

6- Ghith, E. S., & Tolba, F. A. A. (2022, May). Real-Time Implementation of an Enhanced PID CONTROLLER based on Marine Predator Algorithm (MPA) for Micro-robotics System. In 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC) (pp. 40-45). IEEE.