AI-2200 System
This system is designed for larger buildings with three to eight cars in the elevator group. It suits buildings with dynamic traffic conditions throughout the day and peak carrying times.
Design RoomElevatorsGPM-IIIAI
Series GPM-III Passenger Elevators
Advanced AI Supervisory Control
Mitsubishi Electric's AI Supervisory Control is the key factor in creating an ideal
elevator system with optimum user service. Two basic systems are available,
and between them they offer a wide range of special functions to suit the
needs of any type of building.
AI-21 System
This system is designed for small or medium size buildings with two to four
cars in the elevator group.
AI-2200 System
This system is designed for larger buildings with three to eight cars in the
elevator group. It suits buildings with dynamic traffic conditions throughout
the day and peak carrying times.
Expert System and Fuzzy Logic
The brain of the AI Supervisory Control employs an intelligent Expert
System that uses the practical knowledge and experience of elevator
group control experts. This information is stored in the system's memory as
a "Knowledge Database." Drawing from this database, various traffic
conditions are monitored and analyzed applying IF-THEN decision rules to
maximize the effectiveness of each elevator operation. Mitsubishi Electric
applies fuzzy logic that enables the elevator control system
to make decisions using fragmentary and fuzzy logic intelligence concepts.
For example, using its "intelligence" and "common sense," the system can
determine whether or not potential car assignments will result in longer
waiting times for calls in the near future or cause elevator congestion. The
assessment results are applied to determine the car assignments in order to
improve overall service.
Configuration of AI-2200 System
Psychological Waiting Time Evaluation
This evaluation function is Mitsubishi Electric technology that originates from
the psychological thought patterns of a passenger waiting for an elevator:
the irritation of a passenger waiting for elevator arrival is proportional to the
square of the actual waiting time. Elevator assignments to hall calls are
performed on the basis of evaluation results. In addition to forecasted
waiting time, factors such as probability of being bypassed for a hall call,
probable time required for traveling after car assignment, current car load
and others are applied in the evaluation function owing to its
coefficient diversity. Car assignments to hall calls are made as a sum of all
factors.
Strategic Overall Assignment
Combining all building traffic conditions, the system forecasts where
future service will be needed and assigns cars accordingly. This greatly
reduces the average overall waiting time and provides optimum service to
passengers throughout the building. Once all car and hall calls are
serviced, the system forecasts where the next calls for service will arise and
assigns the cars so that the waiting time for future passengers is
also reduced. (Strategic Overall Spotting)
Dynamic Rule-set Optimizer
Elevator control (car allocation) performed using ideal rule-set
This system predicts elevator traffic using Neural Networks technology.
According to traffic predictions, a high speed RISC
(Reduced Instruction Set Computer) runs real-time simulations and selects
the optimal rule-set. This way elevator control (car allocation) is
performed using the ideal rule-set.
Example of rule-set selection with real-time simulation
The diagram below shows an example of rule-set selection for morning-up
peak time. The ideal rule-set is selected every few minutes, according
to traffic conditions in the building.
Distinction of Traffic Flow with Neural Networks
Elevator operation in optimum service patterns is essential for efficient transfer of passengers in a building. To ensure the efficiency, service patterns shall be adjusted according to the passenger flows inside the building, which fluctuate constantly depending on the time zones such as morning up peak, lunchtime and evening down peak. Mitsubishifs 
AI-2200 System adopts the Neural Networks technology as the core tool to precisely recognize the current traffic flow in real time, and to select the optimum service pattern in a timely manner. In this technology, ideal gsets of rulesh are selected for each traffic pattern and for each day of the week, using the passenger flow data calculated by the past records of the car load, as well as the frequency of car and hall calls on each floor. At the same time, traffic flow in the next several minutes is predicted based on both past and current operation data, then the selected rule sets are simulated under the predicted flow in order to determine the optimum service pattern. By accumulating data on actual elevator operations, the accuracy in predicting traffic flows and in selecting optimum service patterns is enhanced, and optimum elevator service can be delivered.
Car Allocation Tuning
The AI-2200 system applies a refined algorithm to improve the
average waiting times at each floor in the building, controlling the
number of elevator cars allocated or parked on the crowded floors during
peak periods in incoming, outgoing and lunchtime traffic. The algorithm
covers the situation of elevator services, the conditions of elevator
operations, etc., as well as the degree of traffic density or flows. The tuning
process is described as follows:
Step 1: The initial number is set as crowds gather.
Step 2: The data of each elevator operation is collected.
Step 3: And then, the initial allocation number is tuned to be increased or
decreased according to the fuzzy rules.
Destination Oriented Prediction System
Press the destination floor, and the service elevator is indicated immediately
When a passenger presses a destination floor button on the newly
developed hall operating panel,
the code letter for the service elevator immediately appears next to the
destination floor button. The passenger knows at a glance which elevator to
board.
Because the Destination Oriented Prediction System analyzes passenger's
destinations to reduce passenger travel times and
minimize congestion, the system greatly improves traffic efficiency,
particularly in peak times. In addition, since the passenger's destination floor
is registered automatically, there's no need to press the floor button again
after boarding the elevator.
Motor Drive Mix
By increasing elevator acceleration between floors, waiting time during
congested periods is reduced. The elevator's acceleration and deceleration
rates are adjusted according to car load and traffic conditions to maximize
driving system efficiency.
Immediate Prediction Indication
Once a passenger has registered a hall call, the ideal car to respond is
selected, the hall lantern lights and a chime sounds once to indicate
which door will open. As the car approaches, the lantern begins to flash and
the chime sounds twice. This system provides a highly reliable prediction of
car arrival and reduces passenger irritation.
AI-2200.
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