Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Bassi, Joseph Stephen; Dada, Emmanuel Gbenga; Abidemi, Afeez; Oyewola, David Opeoluwa; Khammas, Ban Mohammed

doi:10.1016/j.heliyon.2022.e09399

Cited by 134 publications

(69 citation statements)

References 379 publications

(292 reference statements)

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“…The decrement of the predictive error through the optimization process provides the PI control gains that fulfill the regulation task in the prediction time horizon. At the end of this stage, the optimum controller parameter vector K * ( tl ) is set to the control system in (12) for the velocity regulation of the actual BLDC motor in the next time interval [ tl − tl +1 ].…”

Section: Predictive Stage In the Bldc Motormentioning

confidence: 99%

“…The efficiency in the regulation and tracking performance of BLDC motors depends on two main aspects, the controller design [8,11] and the controller tuning process [12][13][14][15]. Several PID-like controllers and advanced control strategies have been adopted to address the first aspect.…”

Section: Introductionmentioning

confidence: 99%

“…In the last decade, computational intelligence in the controller tuning task has increased because of the growth of complex demands in real applications that must simultaneously be satisfied. Among the computational intelligence techniques, metaheuristic algorithms [28] have been adopted in the controller tuning problem [12] because they can provide suitable solutions for NP-hard problems.…”

Section: Introductionmentioning

confidence: 99%

“…Output: Effective speed regulation of the BLDC motor. 1 t ← 0, t opt ← 0 2 while t < t f do Compute and apply the PI control action(12) with the gains K * .Run CODE to calculate Θ * by solving (15)-(18), considering the past states x( t) with t ∈ [t − ∆w, t], and using the last Θ * in the elitist online adaptation.Run CODE to calculate K * by solving (20)-(25), considering the last optimized BLDC motor parameters Θ * , and using the last K * in the elitist online adaptation.…”

mentioning

confidence: 99%

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Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

et al. 2022

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The efficiency in the controller performance of a BLDC motor in an uncertain environment highly depends on the adaptability of the controller gains. In this paper, the chaotic adaptive tuning strategy for controller gains (CATSCG) is proposed for the speed regulation of BLDC motors. The CATSCG includes two sequential dynamic optimization stages based on identification and predictive processes, and also the use of a novel chaotic online differential evolution (CODE) for providing controller gains at each predefined time interval. Statistical comparative results with other tuning approaches evidence that the use of the chaotic initialization based on the Lozi map included in CODE for the CATSCG can efficiently handle the disturbances in the closed-loop system of the dynamic environment.

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Section: Predictive Stage In the Bldc Motormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

et al. 2022

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“…The major problems associated with ANN include the lengthy and time-consuming training process, while in the FL system, the generation of fuzzy membership functions depends on the proper tuning of the model, data analysis, and knowledge of the operator [8]. To avoid the stated issues, in recent years, researchers have employed recently introduced AI-based optimization techniques to achieve the optimal combination of PID controller gains in AVR systems, among which PSO is an extensively used algorithm owing to its smooth optimization process and simple implementation procedure, as can be seen in references [9][10][11][12]. However, it suffers from a few major drawbacks such as slow convergence in the optimization process, stagnation in the local optima, uncertainty in its parameter selection, and sub-optimal response in some of the very familiar benchmark functions [13].…”

Section: Introductionmentioning

confidence: 99%

Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

et al. 2022

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The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics.

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Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

Sivadasan,

Shiney

2023

Adv Control Appl

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A novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.

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Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Cited by 134 publications

References 379 publications

Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

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