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:
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:
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:
A group controller for controlling elevator cars in a building having a plurality of floors includes a traffic and traffic rate estimator for providing fuzzy estimates of traffic and traffic rate; a closed loop fuzzy logic controller for providing a control parameter in response to the fuzzy estimates of traffic and traffic rate and in response to an elevator control system output variable; and an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions in response to the control parameter.
Abstract:
A group controller for controlling elevator cars in a building having a plurality of floors includes an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions, the elevator dispatcher having a constraint for limiting car assignments in response to the constraint; and an adaptive contraint generator for modifying a value of the constraint in response to an elevator control system output variable. In one embodiment, the group controller includes a traffic and traffic rate estimator for providing fuzzy estimates of traffic and traffic rate; a fuzzy logic controller for providing a control parameter in response to the fuzzy estimates of traffic and traffic rate, the control parameter having a constraint for limiting a value of the control parameter; an adaptive constraint generator for modifying a value of the constraint in response to an elevator control system output variable; and an elevator dispatcher for controlling the operation of the elevator cars during single source traffic conditions in response to the control parameter.
Abstract:
A computer controlled elevator system (FIG. 1 ) using prediction methodology to enhance the system's elevator service, having "learning" capabilities to adapt the system to changing building operational characteristics, including signal processing means for computing the "best" prediction model to be used for prediction, the best factoring coefficients for combining real time and historic predictors associated with the best prediction model, the best data and prediction time interval lengths to be used, and the optimal number of look-ahead intervals or steps (for real time predictions) or look-back days (for historic predictions) to the extent applicable to the prediction model, etc. Using the algorithm(s) of the invention the best prediction methodology and associated parameters are selected by running on site simulations based on exemplary values and comparing the prediction results to recorded data indicative of the actual events that have occurred in the system over a past appropriate period of time. That which provides the most accurate predictions, i.e., those with a minimum error as determined by appropriate mathematical models (e.g., sum of the square of the prediction error or sum of absolute error), are thereafter used in the prediction methodology of the system until further evaluations indicate that further changes should be made.
Abstract:
An elevator system containing a group of elevator cars (1-4) and a group controller (32) having signal processing means (CPU) for controlling the dispatching of the cars from a main floor or lobby (L) in relation to different group parameters. During up-peak conditions, each car is dispatched from the main floor to an individual plurality of contiguous floors, defining a "sector" (SN). Sectors are contiguous, and the number of sectors may be less than the number of cars, and a floor can be assigned to more than one sector. Floors that constitute a sector assigned exclusively to a car are displayed on an indicator (SI) at the lobby. Cars are selected for assignment by grouping floors into sectors and appropriately selecting sectors, so that each elevator car handles more or less an equal predicted traffic volume during varying traffic conditions, resulting in the queue length and waiting time at the lobby being decreased, and the handling capacity of the elevator system increased. Estimation of future traffic flow levels for the various floors for, for example, each five (5) minute interval, are made using traffic levels measured during the past few time intervals on the given day as real time predictors, using a linear exponential smoothing model, and traffic levels measured during similar time intervals on previous days as historic traffic predictors, using a single exonential smoothing model. The combined estimated traffic is then used to group floors into sectors ideally having at least nearly equal traffic volume for each time interval.
Abstract:
Elevator system with multiple cars (1-4) and a group controller (32) having signal processing means (CPU) controlling car dispatching from the lobby (L). During peak conditions (up-peak, down-peak and noontime), each car is dispatched and assigned to hall call floors having a large predicted number of passengers waiting on priority basis, resulting in queue length and waiting time at the lobby and upper floors being decreased, and system handling capacity increased. Estimations of future traffic flow levels for the floors for five minute intervals are made using traffic levels measured during the past few time intervals on that day as real time predictors, using a linear exponential smoothing model, and traffic levels measured during similar time intervals on previous similar days as historic traffic predictors, using a single exponential smoothing model. Combined prediction is used to assign hall calls to cars on priority basis for those floors having predicted high level of passenger traffic to limit maximum waiting time and car load. Noontime priority scheme is based on multiple queue sizes and percentages of maximum waiting time limits. Different waiting time limits can be used for lobby and above lobby up and down hall calls with automatic adjustment. During up-peak the lobby is given high priority. The lobby queue is predicted using passenger arrival rates and expected car arrival times. Down-peak operation uses multiple queue levels and percentages of waiting time limits, with estimated queues based on passenger arrival using car-to-hall-call travel time.