Particle Swarm Optimisation Algorithm Based PID Controller

Asifa, H.M.; Vaishnav, S. R.

doi:10.1109/icetet.2010.145

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Range of Search 32,32] In Table 2, the column named PSO refers to the result achieved by the PSO algorithm, and other columns refer to the result achieved by improved PSO introduced in a number of studies, 34,35,38 and the column named ACPSO refers to the result achieved by the proposed algorithm in this paper.…”

Section: Test Function Dimensionmentioning

confidence: 99%

“…In the study of Gaing, a novel design method for determining the optimal PID controller parameters of an automatic voltage regulator system using the PSO algorithm is presented. In the study of Asifa and Vaishnav, a PSO method for determining the optimal PID controller parameters to improve the step response of a third‐order system is presented. The step response of the given system is defined in peak overshoot, settling time, and steady state error.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive chaos particle swarm optimization for tuning parameters of PID controller

Nie

Wang

Xiao

et al. 2017

Optim Control Appl Methods

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Summary An adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional‐integral‐derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller.

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Section: Test Function Dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive chaos particle swarm optimization for tuning parameters of PID controller

Nie

Wang

Xiao

et al. 2017

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…PID tuning with particle-based optimization algorithms shows promising results on various occasions [6][7][8][9]. A PSObased PID controller performs better than one tuned with a conventional tuning method, i.e.…”

Section: Introductionmentioning

confidence: 99%

Particle-Based Optimization Algorithms for Longitudinal Control of Autonomous Vehicle: A Comparative Study

Maani,

Mahdi,

Arfian

et al. 2023

Int. J. Automot. Mech. Eng.

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In order to improve the stability and performance of an autonomous vehicle, optimization needs to be explicitly performed in the controllers, which has an essential part in the tracking system. This work proposes a novel longitudinal control optimization scheme and a novel longitudinal controller consisting of a feed-forward and feedback term. The feed-forward term is inspired by the vehicle’s steady-state response, whereas the feedback term is a proportional-integral-derivative (PID) controller. Also, a model representing the longitudinal vehicle dynamics is designed based on physical phenomena affecting the vehicle. Besides, some nature-inspired optimization algorithms are used to determine the optimal model parameters and optimize the controller parameters, i.e., Particle Swarm Optimization (PSO), Accelerated PSO (APSO), Flower Pollination Algorithm (FPA), and Modified FPA (MFPA). The algorithms are compared in optimizing the longitudinal vehicle model and controller using the CARLA simulator, and stability tests are also done for each algorithm. In addition, the characteristics of several cost functions in controller optimization are inspected. The results show that the MFPA is the most stable algorithm, the proposed model represents the system satisfactorily, and optimizing the controller using a regularized cost function leads to better overall performance. Our code is available in https://github.com/fadamsyah/Particle-Based-Optimization-for-Longitudinal-Control.

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“…Tais algoritmos são inspirados por eventos e comportamentos naturais, o algoritmo particle swarm optimization (PSO) tem sido aplicado abertamente em diversas áreas como a biomédica, processamento de sinais, design de estruturas, otimização combinatória, sistemas de controle e entre muitas outras de acordo com Poli (2008). Em sistemas de controle, o algoritmo PSO tem sido usado com sucesso para ajustar parâmetros de ganho de modo ótimo para a técnica PID ( (Asifa and Vaishnav, 2010) e (Janprom et al, 2020)), alimentando o algoritmo com dados dínamicos do sistema e a força de controle atualização dos ganhos. Para avaliação do algoritmo PSO, de acordo com Akhtaruzzaman and Shafie (2010), o sistema pêndulo invertido é um ótimo exemplo dinâmico para aplicar e avaliar técnicas e sistemas de controle, pois, sua modelagem é analoga a muitos problemas de engenharia e por ser um sistema não linear e de alta ordem e fase mínima.…”

Section: Introdu ç ãOunclassified

Análise de controle via algoritmo particle swarm optimization

Karmouche

Araújo

Preto

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Metaheuristic algorithms have become popular lately as an optimization method due to their similarity to natural adaptation events. In this context, such algorithms have the ability to cover several areas, in which for control systems such algorithms present a new way of developing and adapting control laws that are adapted and improved for different situations in which the dynamic system can be found. This work aims to utilize the metaheuristic algorithm particle swarm optimization in which three populations of gains, generated in different ways, are optimized by the algorithm and adapted for different situations of parameters of the analyzed system. Resumo: Algoritmos meataheurísticos se tornaram populares ultimamente como método de otimização devido a sua similaridade com eventos naturais de adaptação. Neste contexto, tais algoritmos possuem a capacidade de abranger diversas áreas, em que para sistemas de controle tais algoritmos apresentam um novo meio de desenvolver e adaptar leis de controle que são adaptadas e melhoradas para diversas situações em que o sistema dinâmico pode ser encontrado. Este trabalho visa utilizar o algoritmo metaheurístico particle swarm optimization em que três populações de ganhos, gerados de formas diferentes, são otimizados pelo algoritmo e adaptados para diferentes situações de parâmetros do sistema analisado.

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Particle Swarm Optimisation Algorithm Based PID Controller

Cited by 10 publications

References 8 publications

An adaptive chaos particle swarm optimization for tuning parameters of PID controller

An adaptive chaos particle swarm optimization for tuning parameters of PID controller

Particle-Based Optimization Algorithms for Longitudinal Control of Autonomous Vehicle: A Comparative Study

Análise de controle via algoritmo particle swarm optimization

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