Motor Speed Control Using Self-Learned Inertia Estimation

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Solution Overview

Problem

Existing speed control methods for motors in systems like elevators struggle to precisely control motor speed due to varying load torque and moment inertia, leading to suboptimal acceleration performance and transient responses.

Innovation Solution

A self-learning model is established to correlate load torque and moment inertia, allowing for the estimation of moment inertia values through integral operations and lookup tables or curve-fitting relations, enabling adjustment of controller parameters for precise speed control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If parameter estimation is performed using differential operation, then moment inertia can be calculated, but high-frequency noise occurs requiring additional filters

Engineering Contradiction:

Improvemoment inertia estimation accuracyVSAvoidhigh-frequency noise

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent inverts the traditional approach by using integral operation instead of differential operation to estimate moment inertia. This reversal eliminates the high-frequency noise problem that plagues differential-based methods, as integration inherently smooths out noise rather than amplifying it.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent converts the potentially harmful effect of noise into a beneficial outcome by using integral operation. The integration process naturally filters out high-frequency noise while preserving the useful signal, turning what would be a harmful factor into an inherent filtering mechanism.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

2Reliability

If moment inertia is estimated to adjust controller parameters, then motor speed control performance is improved, but the parameter estimation process may affect closed-loop operation

Engineering Contradiction:

Improvemotor speed control performanceVSAvoidclosed-loop operation stability

Core Design Contradiction:

ReliabilityVSStability of the object's composition

Solution Approach 1:

The patent performs parameter estimation during idle periods or transition phases before the main closed-loop control operation. By preliminarily estimating moment inertia when the system is not in critical operation, the controller can prepare optimized parameters without disrupting the stability of ongoing closed-loop control.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses feedback mechanisms to monitor the system state and determine when parameter estimation should be performed. By feedback-based scheduling of estimation operations, the system ensures that parameter updates occur only when they will not compromise closed-loop stability, maintaining both performance improvement and operational stability.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11804793B2Method of speed control based on self-learning model of load torque and moment inertia

Publication Date: 2023.10.31 DELTA ELECTRONICS INC(CN)

AI summary

A method of speed control based on a self-learning model of load torque and a moment inertia is applied to a controller of controlling a motor. The method includes steps of: establishing a relationship between the load torque and the moment inertia by a self-learning manner, correspondingly acquiring a value of the moment inertia according to a value of the load torque, and adjusting parameters of the controller to control rotation of the motor according to the value of the moment inertia.