Fuzzy Logic in Elevator Group Control System enhances efficiency by handling complex variables such as traffic patterns and passenger demands. It mimics human decision-making, optimizing elevator dispatch and reducing waiting times. This adaptive approach improves overall system performance, leading to increased user satisfaction and operational cost savings.
The idea of fuzzy logic was first introduced by Lotfi Zadeh, a professor at the University of California. Elevators are the primary means of transportation that is used nowadays in tower buildings. The application of fuzzy logic to elevator control systems has made the trend higher and better.
Basically, the elevator group control system needs to decide which of the several cars in a group should respond to a call made by a passenger by pressing a hall call button and providing the best possible level of service.
Conventional group control systems by using a fixed function for evaluating service are unable to provide optimum service based on the varying traffic conditions.
On the other hand fuzzy logic, the logic of approximate reasoning deals with uncertainty or partially true values. This kind of logic enables computers to use reasoning more appropriately to the problem-solving challenges.
All major elevator companies are using fuzzy logic rules to optimize efficiency and reduce waiting time. For example, Mitsubishi Elevators uses a 32/64 bit microprocessor in the group control system and the fuzzy rules base expresses the elevator control system principal in the form of offline and online using IF-THEN rules. Offline is applied regardless of the occurrence of hall calls. The cars are assigned to crowded floors during traffic time and parking floors when there is no hall call registered. On-line rules are applied when a hall call is registered. Then the system performs fuzzy calculations to determine which rule to apply that best suits the traffic condition.
Neural Network Technology is used for learning traffic patterns to set the best rule for fuzzy logic. The neural network technology has enabled the system to continuously and accurately predict the passenger traffic flow with intervals of time. A high-speed reduced instruction set computer is used to run real-time simulations using multiple rule-sets and the predicted passenger traffic to select the rule set which optimizes the transport efficiency.
Fuzzy logic also provides an improvement in immediate car assignment. With this feature when a passenger calls for an elevator, the despatching system decides immediately which car will be assigned to the passenger. The system announces the specific car assignment immediately by illuminating the hall lantern.
Below are the benefits of applying Fuzzy Logic in the Elevator Group Control System:
- Optimized Performance:
- Improves efficiency by adapting to varying traffic patterns and passenger demands.
- Reduced Waiting Times:
- Minimizes passenger waiting time through intelligent dispatching.
- Enhanced User Satisfaction:
- Provides a smoother and more reliable service experience.
- Energy Efficiency:
- Lowers energy consumption by optimizing elevator movements.
- Scalability:
- Easily adapts to different building sizes and layouts.
- Flexibility:
- Handles complex and dynamic situations more effectively than traditional systems.
- Improved Traffic Management:
- Manages peak times and high traffic efficiently.
- Cost Savings:
- Reduces operational costs through efficient energy and resource management.
- Safety and Reliability:
- Ensures safer operation by handling diverse scenarios intelligently.
- Future-Proof Technology:
- Readily integrates with advanced building management systems.
