Gaussian adaptive PID control optimized via genetic algorithm applied to a step-down DC-DC converter

Puchta, Erickson Diogo Pereira; Lucas, Ricardo; Ferreira, Felipe R. V.; Siqueira, Hugo Valadares; Kaster, Mauricio dos Santos

doi:10.1109/induscon.2016.7874509

Cited by 25 publications

(18 citation statements)

References 6 publications

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“…Additionally, th streamflow from distinct plants must be predicted and the results evaluated. Moreover, the use of bio-inspired optimization methods [76][77][78] is encouraged to optimize the ARMA model and the application of the support vector regression method.…”

Section: Discussionmentioning

confidence: 99%

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

et al. 2020

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Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

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Section: Discussionmentioning

confidence: 99%

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

et al. 2020

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“…Puchta et al [17] reported the use of a new set of parameters, referred to as Linked Parameters that lower the rank of the parameters by tying the adaptive parameters to the linear PID gains. Then, the parameters have the same design references as a previously designed PID but enhance its performance.…”

Section: Gaussian Pid Controllermentioning

confidence: 99%

“…The literature presents many metrics to evaluate the quality of the response of a control system. In this work, we address the Integral Absolute Error (IAE), following the premises defined by previous investigations, which must be minimized [17,20]. The IAE integrates the absolute value of the error over time according to Equation ( 27):…”

Section: Performance Evaluation Metric and Codingmentioning

confidence: 99%

“…The GAPID is a recently proposed technique reported in [17][18][19][20], dating back to the years 2016 up to 2019, and studies about the use of bio-inspired optimization algorithms in the design of this controller are being conducted up to the present. This control strategy is difficult to design as it does not have an algebraic solution for the adaptive parameters of the controller.…”

Section: Introductionmentioning

confidence: 99%

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Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

et al. 2021

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This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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“…These approaches should also be investigated in weekly or daily hydrological series. Also, other input selection methods can be addressed (Belotti, et al 2020b;Siqueira et al, 2020c), such as bio-inspired metaheuristics (Puchta et al, 2016;Puchta et al 2020;Santana et al, 2019;Santos et al, 2018).…”

mentioning

confidence: 99%

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Belotti

Mendes

Leme

et al. 2021

Journal of Hydrology and Hydromechanics

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Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.

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Gaussian adaptive PID control optimized via genetic algorithm applied to a step-down DC-DC converter

Cited by 25 publications

References 6 publications

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

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