Elevator Leveling Control Using Multi-Journey Feedback
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Solution Overview
Problem
Existing elevator controllers struggle to optimize the leveling process at stopping points due to non-uniform elevator behavior caused by factors like heating, environmental changes, or aging, leading to inconsistent positioning and potential readjustment needs.
Innovation Solution
A controller that records and analyzes sets of measured values from multiple journeys to calculate changes in distance and velocity, using a memory to select and update groups of past journeys for optimizing the leveling process, ensuring consistent positioning even with varying elevator behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the controller uses a fixed specified distance X for leveling, then the control system is simple, but the positioning accuracy deteriorates due to non-uniform elevator behavior
Solution Approach 1:
The controller records actual leveling distances from multiple past journeys and uses this feedback to calculate a corrected specified distance X'. This feedback mechanism allows the system to adapt to non-uniform elevator behavior while maintaining relatively simple control logic, resolving the contradiction between positioning accuracy and system complexity.
Solution Approach 2:
The controller dynamically adjusts the specified distance parameter X based on recorded operational data, transforming it from a fixed value to an adaptive parameter. This parameter change enables the system to compensate for behavioral variations without requiring complex control architecture.
2Measurement precision
If the controller records and analyzes multiple journeys to optimize positioning, then the positioning accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The controller performs data collection and analysis during idle periods between journeys, preparing correction values in advance. This preliminary action ensures that when a new journey begins, the optimized positioning parameters are already ready, minimizing processing time delays while maintaining high positioning accuracy.
3Reliability
If the controller adapts to changing elevator behavior, then the reliability of positioning improves, but the system complexity increases
Solution Approach 1:
The controller automatically records, analyzes, and adjusts positioning parameters without external intervention. This self-service capability enables the system to adapt to changing elevator behavior and maintain high reliability while keeping the control architecture relatively simple, as no additional complex adaptation mechanisms are required.
4Measurement precision
If the controller uses more past journeys for calculation, then the positioning accuracy improves, but the memory requirements and processing complexity increase
Solution Approach 1:
The controller focuses on recording and analyzing specific critical parameters from past journeys rather than storing complete journey data. This selective approach maintains high positioning accuracy by capturing essential information while minimizing memory requirements and processing complexity.
Data Source
AI summary
The controller according to the invention is a controller for optimizing the leveling process (leveling) of an elevator car at a stopping point for an elevator having an elevator controller which controls the stop of the car at a stopping point as a function of a specified distance X to the stopping point, wherein the controller is designed, during each of at least two, in particular at least three journeys, to record a set of at least one same measured value, to determine a change dX or a changed distance X′ as a function of the set of the current journey and at least one past journey and to output the changed value X′ or the difference dX to the specified distance X.