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

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.

Author(s):  
Ghassan A. Sultan ◽  
Muhammed K. Jarjes

<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>


2018 ◽  
Vol 39 (3) ◽  
pp. 761-771
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou

This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the traditional genetic algorithm will not only have a premature convergence, but may also be trapped in the local optima. In the proposed fuzzy adaptive cuckoo search, all parameters of the PID controller and fuzzy rules are real coded to 75 bits in the optimization problem. Moreover, a fuzzy adaptive strategy is proposed for dynamically adjusting the learning parameters in the fuzzy adaptive cuckoo search, and this indeed enables global convergence. Experimental results verify that the proposed fuzzy adaptive cuckoo search algorithm can shorten the computing time in the evolution process and increase accuracy in the multiobjective optimization problem.


Sign in / Sign up

Export Citation Format

Share Document