An Elevator Group Control System With a Self-Tuning Fuzzy Logic Group Controller

Jamaludin, Jafferi; Rahim, N.A.; Hew, W.P.

doi:10.1109/tie.2010.2044117

Cited by 34 publications

(17 citation statements)

References 17 publications

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“…It is the case of Jamaludin et al (2010) that have presented a fuzzy controller that instead of depending heavily on the predicted passenger traffic pattern for adaptation, the fuzzy logic group controller adjusts itself to suit the system's environment through a self-tuning scheme. Results are simulated and compared to conventional approaches showing a significant improvement.…”

Section: Related Workmentioning

confidence: 99%

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Bolat

Altun

Cortés

2013

Applied Soft Computing

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Abstract.-High-rise buildings require the installation of complex elevator group control systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm is compared to other soft computing techniques such as genetic algorithm and tabu search approaches that have been proved as efficient algorithms for this problem. The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches.

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Section: Related Workmentioning

confidence: 99%

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Bolat

Altun

Cortés

2013

Applied Soft Computing

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“…The most popular approach used before is to select them based on the expert's experience. A linguistic variable can mean different things to different people [3], resulting in different interpretation for MFs and rules, and thus different control performance may be produced [4]. Many researchers have made contributions to multivariable fuzzy controls.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Fuzzy Control for Prosthetic Hands

Duan

Luo²,

Deng³

2015

Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

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Abstract. Multi-freedom prosthetic hand is multivariable system which has many inputs and outputs. The design of fuzzy logic controller is very complex because so many fuzzy control systems have to be designed based on conventional idea. Thus, a multivariable fuzzy control is proposed. An effective strategy is that all variables share a common membership function and a rule base. It reduces complexity of membership function and rule base. Finally, the proposed multivariable Fuzzy logic controller is used to control a multi-freedom prosthetic hand. The control performance shows that the multivariable is effective.

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“…Tabu search has attracted less attention than genetic algorithms, although recently two algorithms based on deterministic and probabilistic approaches have been presented to deal with the problem (Bolat et al, 2011). Lastly, control systems based on fuzzy logic are being enthusiastically tried recently (Jamaludin et al, 2010;Rashid et al, 2011; too.…”

Section: Introductionmentioning

confidence: 99%

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Cortés

Onieva

Muñuzuri

et al. 2013

Computers & Industrial Engineering

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Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.Keywords: Elevator; lift; viral system; bio-inspired algorithms; elevator group control system; vertical transportation AcknowledgmentsThe authors acknowledge the financial support provided by the Spanish Plan for Research and Development Technology and Innovation Plan through its Programme Industrial Production and Design, project Ref. DPI2010-15352. A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.

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An Elevator Group Control System With a Self-Tuning Fuzzy Logic Group Controller

Cited by 34 publications

References 17 publications

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Multivariable Fuzzy Control for Prosthetic Hands

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

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