Performance analysis of an adaptive Gaussian nonlinear PID control applied to a step-down CC-CC converter

Lucas, Ricardo; Oliveira, Robson Moreira de; Nascimento, Claudinor Bitencourt; Kaster, Mauricio dos Santos

doi:10.1109/isie.2015.7281612

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Gaussian functions are smooth and have smooth derivatives, which are characteristics that avoid instability issues caused by abrupt changes in the control signal. The use of Gaussian functions for adaptive PID controllers has been previously discussed in Puchta et al (2020) and Lucas et al (2015). ( ) f ; and P q , I q , and D q are the parameters that define the concavity openness of the curves for P f , I f , and D f , respectively.…”

Section: Pid Controller Parameter Tuning Problemmentioning

confidence: 99%

A Hybrid Bio-Inspired Metaheuristic Applied to PID Controller Parameter Tuning

Rodrigues¹,

Gomes²

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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In this paper, a hybrid population-based, bio-inspired metaheuristic is applied to solve the PID controller parameter tuning problem. The applied algorithm, denoted by CSASOS, is a multi-population metaheuristic that combines good characteristics of the Crow Search Algorithm (CSA) and the Symbiotic Organisms Search (SOS). In CSASOS, one subpopulation follows the search rules of the CSA algorithm and is responsible for finding promising regions in the search space. The second subpopulation follows the search rules of the SOS algorithm and is responsible for performing a refined search in the promising regions to find the best solution for the optimization problem under consideration. Numerical experiments are carried out in order to compare the performance of CSASOS with the performance of the stand-alone versions of both CSA and SOS in the solution of a PDI controller parameter tuning problem, which is a common problem that appears in many industrial applications. The results show that CSASOS presented a better performance in comparison with the competing algorithms.

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Section: Pid Controller Parameter Tuning Problemmentioning

confidence: 99%

A Hybrid Bio-Inspired Metaheuristic Applied to PID Controller Parameter Tuning

Rodrigues¹,

Gomes²

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…The Gaussian Adaptive PID (GAPID) is a kind of adaptive control firstly proposed by this research group in 2015 [16], where the adaptive rule follows a Gaussian function of the system error. A Gaussian curve allows for transition from low to high gain in a smooth fashion.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

Borges

Guerreiro

Monteiro

et al. 2022

Sensors

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This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID.

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Fuzzy-based nonlinear PID controller and its application to CSTR

Jin

2018

Korean J. Chem. Eng.

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Performance analysis of an adaptive Gaussian nonlinear PID control applied to a step-down CC-CC converter

Cited by 5 publications

References 6 publications

A Hybrid Bio-Inspired Metaheuristic Applied to PID Controller Parameter Tuning

A Hybrid Bio-Inspired Metaheuristic Applied to PID Controller Parameter Tuning

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

Fuzzy-based nonlinear PID controller and its application to CSTR

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