The Application of Immune Genetic Algorithm in PID Parameter Optimization for Level Control System

Abstract

This work discusses the use of an Evolvable Proportional-Integral-Derivative (PID) controller that consists of an evolvable PID controller hardware whose gains can be set by Evolutionary Computation techniques, such as Genetic Algorithms in water level control system. Due to PID controllers' widespread use in industry, tuning procedures for them are always a topic of interest. An evolutionary immune inspired algorithm, named Immune Genetic Algorithm (IGA), is used for tuning the controller so that closed-loop step response specifications are satisfied. Experiments with different processes indicate that the gains obtained through Immune Genetic Algorithm (IGA) may provide better responses than those obtained by the classical Ziegler-Nichols method. The PID Controller based on IGA is realized by the configuration software and LabVIEW. The experiment results show that the water level controlled by the PID controller can response fast and reach the stable state quickly in comparison with that controlled by the conventional PID controller.

Turning Gear Control Based on Intelligent PID Algorithm for Simulation Robotic Fish

“…Intelligent PID algorithm is nonlinear in nature, combining PID algorithm and intelligent technology, and it has a wide range of applications for those controlled objects without a precise mathematical model. The intelligent PID control rules are as follows [5,6]: is sampling error value for the previous sampling time. Rule 1 and rule 2 make the system stable and rapid, rule 3 and rule 4 make the intelligent PID control with the adaptive function of changing parameters [7,8].…”

Untitled

“…Bu sorunları ortadan kaldırmak için ve PID denetleyicinin performansını daha etkili hale getirmek için birçok araştırmacı farklı optimal denetim yöntemlerini kullanarak PID denetleyici tasarlamışlardır. İlk önce genetik algoritmalar (GA) kullanılarak optimal P, I ve D parametreleri belirlenip oluşturulan PID denetleyici sisteme uygulanmış ve başarılı sonuçlar vermiştir [4]. Daha sonra parçacık sürü en iyilemesi (PSO), lineer kuadratik regülator (LQR) gibi optimal denetleyiciler kullanarak yeni bir PID denetleyici tasarlamışlardır [5][6].…”

Interval system analysis and PID-PSO control of boost converter for uncertain load parameter

“…Conventional methods such as Zigeler and Nichols [14] and simplex method [15] are hard to determine optimal PID parameters and usually are not caused good tuning, i.e., it produces surge and big overshoot. Recently, intelligent approaches such as genetic algorithm [16][17] and particle swarm optimization [18] have been proposed for PID optimization.…”