Abstract:
A system for controlling elevator cars in a building having a plurality of floors includes a group controller for controlling operation of the elevator cars. The group controller predicts lobby single source traffic for determined periods. When the predicted traffic is below certain limit, cars are assigned to a lobby hall call on demand after hall call registration. When the predicted traffic is above certain limit, cars are assigned to the lobby hall call at intervals. Accordingly, car assignment is scheduled at those intervals. The schedule interval is varied based on predicted traffic and predicted round trip time of the cars. The cars are assigned to hall calls if they arrive within a schedule window. The schedule window comprises a lower and an upper tolerance that are selected around a scheduled time.
Abstract:
An elevator group supervisory control system for selecting the most suitable car among a plurality of elevators, when a hall call is made, to assign to the hall call, comprising: temporary assigning means for temporarily assigning the car by a conventional method such as a fuzzy group supervisory control based on group data representing states of the elevator system at the moment when a new hall call is made; and a neural net for receiving numerical values converted from group data including the result of judgment of the temporary assigning means and outputting an assignment fitness of each elevator. It decides the most suitable elevator from the output pattern of the neural net to assign to the hall call.
Abstract:
A system including a group controller for controlling the dispatching of elevator cars in a building. The group controller operates by using control parameters stored in its memory. The system records car loads of cars leaving the lobby and the time intervals between their departures and uses fuzzy logic to categorize the car loads and intervals into fuzzy sets. The system determines the lobby traffic and traffic rate using fuzzy relations among car loads, departure intervals, lobby traffic and traffic rate and the fuzzy logic rules. The group controller collects traffic data during operation. The system runs simulations off-line, after single source traffic periods, using the specified control parameter values. The system collects and analyzes performance data to identify significant deviations from acceptable performances. New sets of control parameters are selected using appropriate specified rules. The process of simulation and learning new values of control parameters are repeated until acceptable performance is achieved. The selected parameters are used in system operation. The group controller repeats this process of simulation and learning the parameters periodically.
Abstract:
An elevator control apparatus determines an estimated car delay when the elevator car stops at or passes an elevator hall and controls an operation of the car using the obtained estimated car delay. The elevator control apparatus includes an input data conversion unit for converting traffic data, including position of the car, direction of movement, and car calls and hall calls, such that it can be used as input data of a neural net. An estimated car delay operation unit includes an input layer for taking in the input data, an output layer for outputting the estimated car delay, and an intermediate layer provided between said input and output layers in which a weighting factor is set. An output data conversion unit converts the estimated car delay output from the output layer such that it can be used for a predetermined control operation. The estimated car delay operation unit constituting a neural net.
Abstract:
A traffic volume estimating apparatus 1A estimates the traffic volumes of traffic apparatus, and a traffic flow presuming apparatus 1B presumes the traffic flows generating the estimated traffic volumes. A presumption function constructing apparatus 1C corrects the presumption functions of the traffic flow presuming apparatus 1B on actually measured traffic volumes, traffic flow presumption results and control results. A control result detecting apparatus 1G detects the control results and the drive results of the traffic apparatus. Further, a control parameter setting apparatus 1D sets control parameters on traffic flow presumption results, and corrects the control parameters according to the control results and the drive results.
Abstract:
The present invention is directed to an elevator dispatching system for controlling the assignment of elevator cars. More particularly, the present invention is directed to a method of determining the commencement and/or conclusion of UP-PEAK and DOWN-PEAK periods of operation. For example, for commencing UP-PEAK operation, a lobby boarding rate is predicted, based on historical information of the number of passengers boarding the elevators at the lobby and the number elevators leaving the lobby. The predicted lobby boarding rate is compared with a predetermined threshold value. If the predicted lobby boarding rate is greater than the predetermined threshold value, UP-PEAK is commenced. In the preferred embodiment, the predetermined threshold value is a predetermined percentage of the elevator car's capacity. Additionally, the present invention is directed to a method of adjusting the threshold value based on actual passenger traffic. For example, once UP-PEAK is commenced, the load of the first few elevators leaving the lobby within a predetermined time interval is determined, and the threshold value is adjusted based on their determined load. If the determined load is greater than a certain percentage of the elevator car's capacity, indicative of starting UP-PEAK too late, the threshold value is decreased. Similarly, if the determined load is less than a certain percentage of the elevator car's capacity, indicative of starting UP-PEAK too soon, the threshold value is increased.
Abstract:
An elevator control apparatus includes a fuzzy rule base having fuzzy rules stored therein which govern the selection of an elevator cage to be assigned to respond to a call. A reasoning unit is provided for selecting the appropriate fuzzy rule to be applied to a cage. The reasoning unit selects the appropriate fuzzy rule according to evaluation factors such as the miss forecast rate and the estimation rate of the cages.
Abstract:
A method for controlling an elevator group in which statistical data on a traffic flow within an elevator group, representing the times, local and total volumes of the traffic, and a number of different traffic types used in a group control are stored in a memory unit belonging to the control system. The traffic flow is divided into two or more traffic components, the relative proportion or different traffic components and the prevailing traffic intensity are deduced from the traffic statistics, the traffic components and traffic intensity, i.e. the traffic factors, are subjected to assumptions whose validity is described by means of membership functions of the factors. A set of rules which correspond to different traffic types are formed from these factors and are assigned values by means of the factors and membership functions, the rule which best describes the prevailing traffic is selected, and the traffic type corresponding to the selected rule is used in the control of the elevator group.
Abstract:
A group management method and apparatus for elevators is disclosed. The apparatus includes a car-position predicting device for predicting a car position and a car direction which will have been taken by each car when a predetermined time has elapsed, a predicted-empty-car detecting device for predicting from the predicted car position and direction an empty car which will be available when the predetermined time has elapsed, and an assignment restricting device for restricting the assignment of the predicted empty car to a floor call. In the group management method, a waiting time derived from a registered floor call which is assigned to each car is evaluated, and a car to be assigned to the floor call is selected on the basis of the result of the evaluation.
Abstract:
In a supervisory system for elevators, a learning function for changing and controlling the operations of the elevators so as to be suited to the traffic conditions of a building is provided. The traffic conditions are automatically learned by taking the statistics therefor to revise the learing function program by externally applying revisional information, where alterations of the learning function program conforming to traffic conditions in the building are made possible to render the learning function flexible.