2016
DOI: 10.1016/j.measurement.2015.09.038
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Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

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Cited by 78 publications
(33 citation statements)
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“…Such as the number of particle swarm n , the upper limit value max  and lower limiting value min  of inertia factor, the weighting factor 1 c and 2 c . And the Max Generation m .…”
Section: Initializationmentioning
confidence: 99%
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“…Such as the number of particle swarm n , the upper limit value max  and lower limiting value min  of inertia factor, the weighting factor 1 c and 2 c . And the Max Generation m .…”
Section: Initializationmentioning
confidence: 99%
“…The optimal parameters often can not by manual adjustment. The domestic and foreign scholars have proposed several advanced control methods to improve PID controller, such as intelligent control [2], variable structure control (VSC) [3] and self-tuning PI controllers [4]. Among to intelligent control, particle swarm optimization and various improved methods have been proposed in past literatures.…”
Section: Introductionmentioning
confidence: 99%
“…Also, from the review of the existing literature studies, it can be recognised that only some of the studies focused towards the optimisation of SLIM. Optimization techniques namely, GA [23], [24],PSO [25],cuckoo search algorithm [26], backtracking search algorithm [27], etc., are being used in many studies in order to enhance the performance of IM drives, in particular, its control systems [28]- [30]. In [31], GA was applied to improve the fuzzy-phase plane controller for speed tracking/ ideal position control in IM.…”
Section: Introductionmentioning
confidence: 99%
“…In [34], in order to improve the performance of fuzzy logic speed controller [12], backtracking search algorithm (BSA) was utilised in IMs [27]. Nevertheless, these optimisation techniques mostly had limitations on local minima, global minimum, optima trapping, trial-and-error procedure; further also have drawbacks in expanding the algorithms and reducing the computational time to attain optimal performances through optimisation.…”
Section: Introductionmentioning
confidence: 99%
“…Particle swarm optimization (PSO) has been used to minimize the annual total building energy consumption and to improve the building energy performance [11]. Similarly, fuzzy logic control was improved using the quantum lightning search algorithm and backtracking search algorithm to control an induction motor drive [12,13], and a quantum gravitational search optimization algorithm was used to solve the optimal power quality monitor placement problem in power systems [14]. A variety of methods and optimization techniques have been used recently to help end users create optimal appliance scheduling of energy usage based on different feed-in tariffs, pricing schemes, and comfort settings.…”
Section: Introductionmentioning
confidence: 99%