Multi Car Elevator Control by using Learning Automaton

We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons ( LA s.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an e ffi cient MCE control is di ffi cult for top-down approaches. For example, “bunching up together” is one of the typical phenomenon in a simple tra ffi c environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple tra ffi c control. First, we assign a stochastic automaton ( SA ) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation e ffi ciency is evaluated through simulation experiments. Results show the technique enables reducing waiting times e ffi ciently, and we confirm the system can adapt to the dynamic environment.