Sliding Mode Control of a Wastewater Plant with Neural Networks and Genetic Algorithms

Jaramillo-Morán, Miguel A.; Peguero-Chamizo, Juan C.; Salazar, Enrique Martínez de; Valle, Montserrat García del

doi:10.1007/978-3-540-75271-4_13

Cited by 1 publication

(1 citation statement)

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“…To find the perfect spot where treatment efficacy and operating costs are both satisfied, researchers suggest using an intelligent multi-objective optimization control (IMOOC) based on an adaptive multi-objective differential evolution (AMODE) algorithm to sift through the data. The AMODE method is meant to enhance local search and global exploration capabilities in order to boost optimization efficiency and reach quick convergence [21]. Using an autoregressive with endogenous model, the self-tuning control implementation for a liquid process model ensures that tank levels remain on their desired trajectories, regardless of external shocks or unknowns in the system dynamics.…”

Section: Wastewater Treatmentmentioning

confidence: 99%

Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study

Kumara,

Ganesan

2024

IJ-AI

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Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.

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Section: Wastewater Treatmentmentioning

confidence: 99%