scholarly journals Optimal PID controller design using artificial bee colony algorithm for robot arm

2021 ◽  
Vol 21 (1) ◽  
pp. 84
Author(s):  
Ghassan A. Sultan ◽  
Muhammed K. Jarjes
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Dynamic Performance ◽  
Control Process ◽  
Robot Arm ◽  
Bee Colony ◽  
Pid Controller Design ◽  
Best Parameter

<p class="Default"><span>Proportional integral derivation (PID) controller is used in this paper for optimal design, and tuning by zeigler and nichol (ZN) with artificial bee colony algorithm. The best parameter were found using these algorithms for best performance of a robot arm. The advantage of using ABC were highlighted. The controller using the new algorithm was tested for valid control process. Different colony size has been performed for tuning process, settling time, from time domain performance, rise time, overshot, and steady state error with ABC tuning give better dynamic performance than controller using the (ZN).</span></p>

Robust PID controller design based on H∞ theory and a novel constrained artificial bee colony algorithm

2016 ◽  
Vol 40 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Vahid Bijani ◽  
Alireza Khosravi
Keyword(s):  
Pid Controller ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Robust Performance ◽  
Bee Colony ◽  
New Strategy ◽  
Pid Controller Design ◽  
High Flexibility ◽  
Setting Parameters

This paper presents a new strategy for tuning the coefficients of a PID controller regarding H∞ robust performance and stability constraints based on the constrained artificial bee colony (CABC) algorithm. First, the issue of tuning the PID controller to follow H∞ specifications are introduced, and the objective function and constraints of the optimization problem are specified. Then, a simple and efficient method of transforming the constrained optimization problem to an unconstrained one is presented, and used within the CABC for optimization. The CABC is one of the most recently introduced optimization algorithms, and has the advantages of strong robustness, fast convergence and high flexibility, with fewer setting parameters. The algorithm is essentially a random and intelligent evolutionary method. This method is also utilized and simulated in several models. The simulation results have been compared with other techniques, which demonstrate the efficiency and superiority of the proposed scheme.

Steam Pressure of Foaming Machine of Fuzzy PID Control and Simulation

2012 ◽  
Vol 531-532 ◽  
pp. 726-731
Author(s):  
Yue Hua Xiong ◽  
Chun Liang Zhang ◽  
Bai Xiang Fu
Keyword(s):  
Pid Controller ◽  
Controller Design ◽  
Dynamic Performance ◽  
Steam Pressure ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Tuning Method ◽  
Fuzzy Pid Controller ◽  
Pid Controller Design ◽  
High Control

This paper focus on designing a fuzzy PID controller design about the vapor pressure of the EPE foaming machine parameters, and raise a self-tuning method of PID parameters, and use the fuzzy control toolbox of MATLAB to simulate its control system, which are compared with the simulation of conventional PID controller, the results show the design of fuzzy PID controller have high control precision, small overshoot, good dynamic performance characteristics.

Optimal Tuning of Robust Controller Based on Artificial Bee Colony Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1668-1672 ◽  
Author(s):  
Kun Xu ◽  
Ming Yan Jiang
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Proportional Integral Derivative ◽  
Robust Controller ◽  
Optimal Tuning ◽  
Bee Colony ◽  
Constraint Factors ◽  
Controller Performance ◽  
Swarm Intelligence Algorithm

Artificial bee colony algorithm (ABCA) is a novel swarm intelligence algorithm for global optimization. An efficient method based on ABCA is proposed for optimal tuning of robust proportional-integral-derivative (PID) controller in this paper. A multi-objective optimization is applied to balance several constraint factors of controller. Performance of robust PID controller is evaluated by a three-order and time-delay transfer function with 40%, 50% and 60% uncertainty, respectively. Simulation result clearly demonstrates that the designed controller can obtain an optimal tuning and endure parameter fluctuation. From comparisons of robust PID controller in different uncertainties, a conclusion can be obtained that performance of robust PID controller will be worse as the uncertainty increases, even obtain a divergent solution when the uncertainty is more than 60%.

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