Elevators & Escalators - MITSUBISHI ELECTRIC

Mitsubishi Electric's application of breakthrough Artificial Intelligence technology to elevator control has resulted in improved operational efficiency and new functions that can be configured to suit a variety of building types.

Two basic systems are available, including the AI-21 System and

AI-22 System which are designed for small or medium-sized buildings with two to four cars in the elevator group, and the

AI-2200C System for larger buildings with three to eight cars in the elevator group. The

AI-2200C System is especially suitable for buildings with dynamic traffic conditions throughout the day and peak carrying times.

An intelligent "Expert System" comprises the brains behind AI Supervisory Control. It's called the Expert System because it incorporates the practical knowledge and experience of actual elevator group control experts, forming a "Knowledge Database" stored inside the system's memory. The purpose is to maximize the operational effectiveness of each car, by drawing from the Knowledge Database and applying if-then decision rules to monitor and analyze traffic conditions.

Additionally, the elevator control system can make decisions using fragmentary and fuzzy logic intelligence concepts. Using its built-in "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 contribute to elevator congestion. The assessment results are applied to determine car assignments in order to improve overall service.

In general, people will patiently await a bus or train for ten minutes and even more, but irritation sets in almost immediately if an elevator car doesn't arrive at once. After comprehensive study of this phenomenon, Mitsubishi Electric has formulated a psychological evaluation function based on the following principle:

The irritation of a passenger waiting for elevator car arrival is proportional to the square of the actual waiting time.

Based on this principle, car assignments to hall calls are performed by the AI Supervisory Control System on the basis of evaluation results generated by the sum of such factors as forecasted waiting time, probability of being bypassed for a hall call, probable time required for traveling after car assignment, current car load and others, owing to the evaluation function's coefficient diversity.

The AI Supervisory Control System combines all building traffic conditions to forecast where future service will be needed, then assigns cars accordingly. The result is a considerably reduced overall average waiting time, thus providing optimum service to passengers throughout a building.

Once all car and hall calls are serviced, the system uses Strategic Overall Spotting forecasts to predict where the next calls for service will arise, then assigns the cars so that waiting time for future passengers is also reduced.

Mitsubishi Electric's Dynamic Rule-set Optimizer allocates elevator cars using an ideal set of rules. Neural Networks technology enables the system to predict passenger traffic within intervals of several minutes for continual and accurate forecasting.

A high speed RISC (Reduced Instruction Set Computer) runs real-time simulations with multiple rule-sets and the predicted traffic, then selects the rule-set which optimizes transport efficiency.

During morning up peak time, an ideal rule-set is selected every few minutes according to building traffic conditions.

In order to ensure efficient conveyance of elevator users in a building, elevators must operate consistently with optimum service patterns. Efficiency is maintained by adjusting service patterns to passenger flows within the building, which fluctuate constantly depending on time zones such as morning up peak, lunchtime and evening down peak.

Mitsubishi Electric's

AI-2200C System applies Neural Networks technology to precisely recognize the current traffic flow in real time, and to select the optimum service pattern in a timely manner. It works by applying an ideal rule-set for each traffic pattern and for each day of the week, using the passenger flow data calculated by past car load records, as well as frequency of car and hall calls on each floor.

At the same time, it predicts traffic flow in the next several minutes based on both past and current operating data. The selected rule-sets are simulated under the predicted flow in order to determine the optimum service pattern. Accuracy in predicting traffic flows and selecting optimum service patterns is enhanced through the accumulation of actual elevator operation data, in order to achieve optimum elevator service.

In order to improve average waiting times at each floor in a building, the

AI-2200C System applies a refined algorithm that controls the number of elevator cars allocated or parked on crowded floors during peak periods in incoming, outgoing and lunchtime traffic. The algorithm applies to elevator service situation, operating conditions, the degree of traffic density or flows and other factors. Car Allocation Tuning employs a three-step process, as follows:

The Destination Oriented Prediction System is a more practical way to optimize elevator service. A panel with all available destination floors lets a user press the number of the specific floor he or she is going to, which, in turn provides an immediate indication of the next car to serve the floor.

The Destination Oriented Prediction System analyzes the number of calls made by the hall operating panels and destination floors, in order to minimize a user's waiting and travel time. This greatly enhances transport efficiency, especially during peak use times. Once inside the elevator car, there's no need for users to press floor buttons, as the requested destinations have already been registered.

Mitsubishi Electric's Motor Drive Mix technology is designed to decrease waiting time during congested periods of elevator use. It does so by increasing elevator acceleration between floors. The elevator's acceleration and deceleration rates are adjusted according to car load and traffic conditions to maximize driving system efficiency.

As a means to reduce user irritation while waiting for an elevator car to arrive, the Immediate Prediction Indication system provides a measure of feedback that helps the passenger predict car arrival.

Here's how it works: Once the user has registered a hall call, the first available car for quickest response is designated, the hall lantern lights and a chime sounds once to indicate which elevator door will open. As the car approaches the lantern will begin to flash and the chime will sound twice, indicating imminent arrival.