The Optimization for PID Controller Parameters Based on Genetic Algorithm

Abstract

as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.

GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

“…PID stands for proportional, integral, and derivative and is a classical controller used for several control applications (Metered et al 2015;Kesarkar and Selvaganesan 2015;Niu 2014;Gad et al 2017;Tammam et al 2013;Abdelrassoul et al 2016;Chen and Chang 2006). The PID controllers are so designed that continuous attention of the operator is eliminated.…”

“…The authors observed that the results were promising as compared to the conventional PID method. Niu (2014) had implemented GA-based optimization method to tune PID parameters of the active suspension system. Absolute error control is used as an objective function to tune the PID parameters.…”

“…It is observed that optimization of PID controller applied to suspension system is carried out in single objective nature (Metered et al 2015;Niu 2014) whereas (Kesarkar and Selvaganesan 2015) presented a multi-modal optimization approach and (Gad et al 2017) presented dual objective function optimization. Kalaivani et al (2014) studied a fuzzy logic-based active quarter car suspension system.…”

Improvement of Transient Response of a DC Motor Controller Based on Non-Linear Optimization Techniques

“…This transfer function was then used to analyze the performance of the system and to design proper controllers to meet the design criteria. For the compensator design, the locations of the desired poles were found out from the proposed values of settling time, rise time and percentage overshoot [8,9]. Using root locus, it was found that a lead compensator is required to place poles in the desired locations.…”

Research of PID Algorithm for Valve Controlled Hydraulic Motor Variable Rate Fertilizer Control System

“…In recent years, precision agriculture variable rate fertilizer control strategy mainly around the PID control algorithm [8][9][10][11][12], fuzzy control rules [13][14] and the relevance vector machine [15] and other aspects of the research work commenced in improving the control system performance, increase precision fertilization made a lot of achievements and to promote the domestic development of intelligent variable fertilization seeder. Research process, due to the PID algorithm has the features of a simple structure, robustness and high reliability [16], has been widely used, however, PID parameter tuning is a complex optimization process that determines the performance of the control system, usually Engineering on tuning methods have decay curve method, Ziegler-Nichols step response method, ITSE optimal setting method, rapid titration and relay method and so on. This paper use of the analyzes of mechanism to establish the mathematical model of electro-hydraulic variable rate fertilization system, using Ziegler-Nichols method tuning PID controller parameters, and analysis parameters by testing the impact on system performance.…”