Optimization of PID controller in AVR system by using ant lion optimizer algorithm

Spoljaric, Tomislav; Lusetic, C.; Šimović, Vladimir

doi:10.23919/mipro.2018.8400274

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…To confirm the performance and convergence, the simulation results of autotuning the PID controller for the CPC system are compared. The comparison shows that the proposed method can produce better results than the traditional SLP algorithm, the WOA [24] and the IPSO [45]. According to the simulation results, it can be concluded that the theoretical approach in this paper achieves the performance indices, i.e, t s , t r , M p and e ss , and convergence of the optimal autotuning of the PID parameter suitable for practical applications.…”

Section: Discussionmentioning

confidence: 76%

“…At present, many AI methods have been proposed to autotune the PID parameter, such as fuzzy logic [2], [10], [19], [20], neural networks (NNs) [1], [7], [21], particle swarm optimization (PSO) algorithms [6], [22], [23], hybrid firefly (FA) and pattern search [8], the ant lion optimization (ALO) algorithm [24], the whale optimization algorithm (WOA) [25], cuckoo search (CS) [10], [26], bacterial foraging optimization [27], genetic algorithms [28], the cosine algorithm [29], the bat algorithm [12], ant colony optimization (ACO) [13], [30], differential evolution (DE) [31], World Cup optimization (WCO) [32], evaluation algorithms (EAs) [33], [34], gray wolf optimization (GWO) [35], nature-inspired algorithms [17], chaotic invasive weed optimization [36], [37], flower pollination algorithm (FPA) [38] and firefly algorithm (FFA) [39]. Although many AI methods have been proposed to autotune the PID parameter, the challenges of long execution time and convergence persist.…”

Section: Introductionmentioning

confidence: 99%

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PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

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The proportional integral and derivative (PID) controller is extensively applied in many applications. However, three parameters must be properly adjusted to ensure effective performance of the control system: the proportional gain (K P), integral gain (K I) and derivative gain (K D). Therefore, the aim of this paper is to optimize and improve the stability, convergence and performance in autotuning the PID parameter by using a deterministic Q-SLP algorithm. The proposed method is a combination of the swarm learning process (SLP) algorithm and Q-learning algorithm. The Q-learning algorithm is applied to optimize the weight updating of the SLP algorithm based on the new deterministic rule and closed-loop stabilization of the learning rate. To validate the global optimization of the deterministic rule, it is proven based on the Bellman equation, and the stability of the learning process is proven with respect to the Lyapunov stability theorem. Additionally, to demonstrate the superiority of the performance and convergence in autotuning the PID parameter, simulation results of the proposed method are compared with those based on the central position control (CPC) system using the traditional SLP algorithm, the whale optimization algorithm (WOA) and improved particle swarm optimization (IPSO). The comparison shows that the proposed method can provide results superior to those of the other algorithms with respect to both performance indices and convergence. INDEX TERMS Autotuning gain, central position control system, Q-learning algorithm, PID controller, swarm learning process algorithm, optimal control.

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Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

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“…Hungrier ant lions dig bigger holes which improves their chances of catching prey. The ants slide down the surface of the hole, at which point the antlion consumes it [160] , [161] . Ants move randomly.…”

Section: A Review Of Various Swarm-based Motmentioning

confidence: 99%

“…The, the antlion repositions itself to the caught prey's position. This increases its chances of catching another prey [160] , [161] . Mathematically, the random walk of the prey (ants) can be described as [161] :

011

Where cs is the cumulative sum, n is the maximum number of iterations, t is the iteration step and r is a randomized number in the range [

01

].…”

Section: A Review Of Various Swarm-based Motmentioning

confidence: 99%

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

Reddy

Saha

2022

Heliyon

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“…Ekinci, Hekimoglu, and Eker [7] introduced a Harris Hawks Optimization method as a novel parameter approach to optimize the gains of a PID controller. Spoljarić, Lušetić, and Simovic [8] applied an ant lion optimizer algorithm for optimal tuning of the AVR system in a closed-loop generator control design. Blondin, Sanchis, Sicard, and Dura [9] proposed an optimized gain tuning method for the PID controller based on a fusion between an Ant Colony Optimization algorithm and a Nelder-Mead method.…”

Section: Introductionmentioning

confidence: 99%

Control of the Automatic Voltage Regulator System with a Novel Stability-based Artificial Intelligence Method

Tabakh

Tiryaki

2021

ELEKTRON ELEKTROTECH

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This paper proposes a novel Stability-Based Artificial Intelligence Method for predicting the optimum parameters of the proportional-integral-derivative controller in an automatic voltage regulator system. To implement the stability-based artificial intelligence method, first, parameters which are of great importance for the control of the system are applied to the system randomly, data are collected, and then artificial intelligence studies are carried out. The suggested approach has been applied to the system and compared with other control methods in the literature, namely the improved Kidney Inspired algorithm, Jaya algorithm, Tree Seed algorithm, Water Wave Optimization, and Biography-Based Optimization to test the robustness of the new method. The numerical results indicate that the proposed method significantly outperforms all other methods.

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Optimization of PID controller in AVR system by using ant lion optimizer algorithm

Cited by 8 publications

References 5 publications

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

Control of the Automatic Voltage Regulator System with a Novel Stability-based Artificial Intelligence Method

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