2018
DOI: 10.1109/tla.2018.8327390
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Optimum Cascade Control Tuning of a Hydraulic Actuator Based on Firefly Metaheuristic Algorithm

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Cited by 6 publications
(5 citation statements)
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“…Research is still in progress to mitigate the deficiencies. Control strategy is one such field, with recent publications highlighting the significance of adaptive control (AC) [15], fuzzy control (FC) [16], feedback linearization control (FLC) [17], sliding mode control (SMC), and its improved extension, e.g., merging with proportion integration differentiation (PID) control [18,19], cascade control (CC) [20], etc.…”
Section: Introductionmentioning
confidence: 99%
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“…Research is still in progress to mitigate the deficiencies. Control strategy is one such field, with recent publications highlighting the significance of adaptive control (AC) [15], fuzzy control (FC) [16], feedback linearization control (FLC) [17], sliding mode control (SMC), and its improved extension, e.g., merging with proportion integration differentiation (PID) control [18,19], cascade control (CC) [20], etc.…”
Section: Introductionmentioning
confidence: 99%
“…be the starting state to convey the deviation of e toward 0. Recall that the damping ratio close to 0 indicates a better working performance in the initial phase according to Equation(20). Thus, we have:…”
mentioning
confidence: 99%
“…Fuzzy logic in combination with sliding model control has also been proposed in [14] to account for inherent nonlinearities of hydraulic actuator dynamic model. Metaheuristic approaches have also been successfully employed for the hydraulic actuator control optimization tasks, such as the firefly metaheuristic algorithm [15], and genetic algorithm presented in [16]. Reference [17] proposed a hybrid optimisation algorithm for a hydraulic cylinder pressure self-tuning PID controller, wherein adaptation has been based on particle swarm optimisation (PSO) combined with genetic algorithm (GA).…”
Section: Introductionmentioning
confidence: 99%
“…FA developed by Yang [22] is a recent nature-inspired metaheuristic algorithm for global optimization. This swarm intelligence mimics the flashing behavior and attraction characteristics of fireflies in nature [23]. However, the classical FA has the premature convergence issue for solving computationally extensive engineering problems.…”
Section: Introductionmentioning
confidence: 99%
“…However, the classical FA has the premature convergence issue for solving computationally extensive engineering problems. Although some modified and hybrid FAs have been proposed [23]- [25], these studies neither qualify the FAs using the Taguchi method nor employ a ranking mutation. This paper employs the robust Taguchi method and mutation operation to improve FA searching performance.…”
Section: Introductionmentioning
confidence: 99%