A Novel Moth-Flame Algorithm for PID-Controlled Processes With Time Delay (original) (raw)
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PID controller design for time delay systems using genetic algorithms
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Proportional-Integral-Derivative (PID) control method has been utilized in many industrial control applications, which has its limitations for the optimized control. Manually calculated controller tunings by using fixed formulas are only adequately to give basic control but not well-competent to non-linear process with various operating levels. Optimization analysis via metaheuristic approach has substantially obtained the best solutions and has proven its credibility to provide the better controller values. Nevertheless, the optimization analysis might provides bad result if the determinant parameter settings are not properly adjusted during the optimization analysis. This paper objectively presents a direct way to determine the best determinant parameter settings for the optimization analysis, whereby the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are chosen for the research analysis. In detail, parameter settings that include upper and lower Bounds (UB, LB), maximum iteration (MaxIt), population size (nPop), mutation rate (mu) and damping ratio (wdamp) are analyzed. Ultimately, Springer Nature 2021 L A T E X template 2 Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings analysis results are compared with manually calculated controller tunings by using Level Process Control Training Module, SE-207. Both performance indexes and response curves showed that PSO and GA were better performed than the conventional tunings. While comparing optimization analyses, PSO reacted more aggressively than GA corresponding to slight overshoots but both optimizations produced very close error values. In conclusion, metaheuristic approach with proper parameter settings produces better control tunings as for the controlled loop demands aggressive control action would prefer PSO, and in contrast, a stabilized control prefers to apply GA for the nonlinear process.
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The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers are some of the reasons for their high popularity and acceptance for control in process industries around the world today. Tuning of PID control parameters has been a field of active research and still is. The primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time. With exception of two popular conventional tuning strategies (Ziegler Nichols closed loop oscillation and Cohen-Coon's process reaction curve) several other methods have been employed for tuning. This work accords a thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms. Methods appraised are categorized into classical and metaheuristic optimization methods for PID parameters tuning purposes. Details of some metaheuristic algorithms, methods of application, equations and implementation flowcharts/algorithms are presented. Some open problems for future research are also presented. The major goal of this work is to proffer a comprehensive reference source for researchers and scholars working on PID controllers.
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Optimal Tuning of PID Controller Using Genetic Algorithm and Swarm Techniques
In order to control the systems, a few control strategies must deal with the effects of non-linearties or uncertainties. As earlier utilized control techniques based on mathematical models have been primarily concentrated on stability robustness against the ill-effects of control mechanism, they are limited in their ability to amend the transient responses. These conventional techniques failed to tune the non-linear and non-minimum phase systems. Therefore, a few modern algorithms have been introduced here such as; Bacteria Foraging Optimization, Particle Swarm Optimization and Genetic Algorithm which have been proved an appropriate aid to improve the transient responses of systems perturbed by non-linearties or unknown mathematical characteristics. This Paper presents designing a PID controller by selection of PID parameters using Bacterial Foraging Optimization, Particle Swarm Optimization (PSO) and Genetic Algorithm. Here, the closed loop step response of the PID controller has been compared for the above mentioned three optimization algorithms.
Simulation and Tuning of PID Controllers using Evolutionary Algorithms For different systems
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PID controllers are used for decades in controlling processes in linear feedback control systems. Their use requires accurate and effective tuning to satisfy an acceptable performance for the control system. This paper presents an automatic procedure for adjusting the gains of a Proportional-Integral-Derivative (PID) controller. Genetic Algorithms are used for tuning this controller so that closed-loop step response specifications are satisfied. By using this procedure, designers need only specify the desired closed-loop response. Experiments with different processes application indicate that the gains obtained through genetic algorithms may provide better responses than those obtained by the classical Ziegler-Nichols method. Moreover, the genetic algorithm is capable of generating adequate gains for systems where classical rules are not applicable. The algorithms are simulated with MATLAB programming.The simulation result showing a better dynamic performance than those based on tra...