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- W2583809378 abstract "The research focused on optimizing parameters of a PID controller using metaheuristics, aimed at improving the response of PID controls, especially those where processes manifest a pronounced nonlinear behavior and for which traditional tuning methods are usually inefficient. Three population-based metaheuristics were selected: Genetic Algorithm (GA), particle swarm optimization (PSO) and the method of cross entropy (CE). The algorithms associated with each metaheuristic were developed in MatLab, together with the code that allows making simulations as case studies, evaluating the performance of each algorithm on different simulated processes. Finally an implementation was performed in a physical controller (PLC) connected to a physical process, in order to compare the performance of the PID parameters obtained by the Metaheuristics versus traditional tuning methods. The results show that the use of metaheuristics to optimize PID controllers allow to obtain a control efficiency comparable or superior to conventional techniques." @default.
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- W2583809378 date "2016-10-01" @default.
- W2583809378 modified "2023-09-25" @default.
- W2583809378 title "Parameters optimization of PID controllers using metaheuristics with physical implementation" @default.
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- W2583809378 doi "https://doi.org/10.1109/sccc.2016.7836043" @default.
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