Simulation of Elevator Group Control Systems

In building automation, efficient scheduling of elevator group control systems directly impacts user experience and energy consumption control. MATLAB simulations enable low-cost validation of different scheduling strategies, with core logic typically comprising the following module designs:

Demand Modeling Simulations must model random passenger calls in multi-floor scenarios (using Poisson distribution to generate requests) while recording metadata such as timestamps, origin/destination floors. During peak hours, request frequency parameters can be adjusted to test system limits. Implementation typically involves creating a request generator function with Poisson-distributed inter-arrival times.

Elevator Behavior Simulation Each elevator maintains its state (running/stopped/faulty), current floor, movement direction, and internal request queue as independent objects. Acceleration constraints must be implemented to prevent physical distortion from sudden stops/starts. This can be achieved through kinematic equations in the elevator motion controller.

Scheduling Algorithm Layer This represents the core competitive aspect of simulations. Classic methods like Shortest Seek Time First (SSTF)/SCAN and Minimum Waiting Time (WT) can be compared with modern reinforcement learning algorithms. The algorithm must respond to new requests in real-time and handle conflicts (e.g., multiple elevators arriving at the same floor simultaneously). Code implementation often uses priority queues and conflict resolution functions.

Visualization Feedback MATLAB's App Designer or animation tools can intuitively display elevator movement heatmaps, average waiting time curves, and other metrics to help quickly identify scheduling bottlenecks. Developers can utilize MATLAB's graphics functions to create real-time animation plots showing elevator positions.

Parameter Tuning Batch simulations test different combinations of parameters such as elevator quantity, speed, and start-stop delays. Optimization methods like genetic algorithms can be combined to find Pareto optimal solutions balancing cost and efficiency. This involves creating parameter sweep scripts and multi-objective optimization functions.

Such simulations provide preliminary validation value for Hardware-in-the-Loop (HIL) testing and digital twin implementation, with potential future extensions incorporating energy consumption models or emergency situation handling logic.