Optimal control of elevators†

Levy, Dalit; Yadin, M.; Alexandrovitz, A.

doi:10.1080/00207727708942042

Cited by 29 publications

(7 citation statements)

References 2 publications

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“…As examples, we can cite Levy et al [10] who used dynamic programming to minimize the expected length of a busy period of an elevator. Pepyne and Cassandras [14] used Markov decision theory to show that the optimal policy for controlling uppeak traffic (moves from first floor up) closes the doors after a critical number of passengers have entered the elevator.…”

Section: Introductionmentioning

confidence: 99%

Optimal parking of idle elevators under myopic and state-dependent policies

Parlar¹,

Moosa²,

Ou³

2006

European Journal of Operational Research

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Section: Introductionmentioning

confidence: 99%

Optimal parking of idle elevators under myopic and state-dependent policies

Parlar¹,

Moosa²,

Ou³

2006

European Journal of Operational Research

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“…1 shows an example of a tall crane whose payload may bounce. One area where cable-stretch bouncing has received some attention is elevator control [15]- [18]. However, cranes with extensible cables have challenges that Manuscript do no arise in elevator control.…”

Section: Introductionmentioning

confidence: 99%

Control of Crane Payloads That Bounce During Hoisting

Yoon

Nation

Singhose

et al. 2014

IEEE Trans. Contr. Syst. Technol.

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Crane payloads exhibit unwanted motions such as swings, twists, and bounces that cause safety hazards and decrease performance. Numerous controllers have been developed to reduce payload swing. However, much less consideration has been given to bouncing in the vertical direction, which in some cases can also excite pitching of the payload. This dynamic effect most often arises when the payload is heavy and the suspension cables are long. This brief presents a mathematical model of a bouncing crane payload. Then, a method for generating commands to suppress the oscillations is developed and evaluated. Experiments on a tower crane demonstrate the improved performance provided by the proposed control method. Index Terms-Control design, open loop systems, vibrations.

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“…Levy et al used dynamic programming (DP) to minimize the expected time needed for completion of the current busy period (8) . To keep the system manageable, the number of buttons that can be on simultaneously is restricted and unlimited capacity of elevator cars is considered.…”

Section: Related Strategiesmentioning

confidence: 99%

Elevator Group Control Using Multiagent Task-Oriented Reinforcement Learning

2005

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In this paper, a reinforcement learning method is proposed that optimizes passenger service in elevator group systems. Task-oriented reinforcement learning using multiple agents is applied in the control system in allocating immediate landing calls to the elevators and operating them intelligently in attaining better service in this stochastic dynamic domain. The proposed system shows better adaptive performance in different traffic profiles with faster convergence compared to the other learning elevator group control system.

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Optimal control of elevators†

Cited by 29 publications

References 2 publications

Optimal parking of idle elevators under myopic and state-dependent policies

Optimal parking of idle elevators under myopic and state-dependent policies

Control of Crane Payloads That Bounce During Hoisting

Elevator Group Control Using Multiagent Task-Oriented Reinforcement Learning

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