Adaptive Motor Control Model Switching for Reliability
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Solution Overview
Problem
Existing motor control methods rely on inaccurate nonlinear models, which can lead to poor reliability due to difficulties in constructing and measuring motor parameters, making it challenging to achieve precise control.
Innovation Solution
A method that adaptively switches between linear and nonlinear models based on the motor's operating region, using a linear model for linear regions and a neural network model for nonlinear regions, with the neural network model trained using historical data and a back propagation algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a nonlinear model is used to approximate the motor's physical model, then the control can adapt to nonlinear operations, but the reliability and accuracy of the model deteriorate due to construction difficulties and parameter measurement errors
Solution Approach 1:
The motor operating region is divided into multiple segments: linear region and nonlinear region. Different control models are applied to different segments - linear control model for linear region and nonlinear control model for nonlinear region. This segmentation resolves the contradiction by allowing each segment to use the most appropriate model, maintaining high reliability in the linear region while achieving nonlinear adaptability in the nonlinear region.
Solution Approach 2:
The control system dynamically switches between linear and nonlinear control models based on the real-time operating region of the motor. The system continuously monitors motor parameters and adaptively selects the appropriate control model, transforming from a static single-model approach to a dynamic multi-model approach, thereby resolving the contradiction between reliability and nonlinear adaptability.
2Adaptability or versatility
If a single nonlinear model is used for all operating regions, then nonlinear control is achieved, but the control precision deteriorates in linear regions where linear models would be more accurate
Solution Approach 1:
Different control models with appropriate qualities are applied to different operating regions. The linear control model is used in the linear region where it provides high precision, while the nonlinear control model is used in the nonlinear region where it provides appropriate adaptability. This local quality assignment resolves the contradiction by optimizing control precision for each specific operating region.
3Adaptability or versatility
If complex nonlinear parameters are measured for model construction, then the model can represent the motor's nonlinear characteristics, but the measurement accuracy deteriorates due to the difficulty of measuring nonlinear parameters
Solution Approach 1:
The parameter measurement and model construction process is segmented into two parts: linear parameters measured with high precision for the linear region model, and nonlinear parameters measured with acceptable precision for the nonlinear region model. This segmentation reduces the overall measurement difficulty while maintaining sufficient model accuracy for each region.
Data Source
AI summary
A method for adaptive motor control includes acquiring current parameters in an operation process of the motor at a current moment; determining a type of a region in which the motor operates at the current moment according to the current parameters; triggering a corresponding motor model according to the type of the region in which the motor operates at the current moment; and inputting the current parameters into the corresponding motor model, generating control parameters for motor operation according to the current parameters, and controlling the operation of the motor according to the control parameters for motor operation. An apparatus and a computer-readable storage medium are also disclosed. In comparison with the conventional motor control which uses the single nonlinear model, the motor control method disclosed herein can greatly improve the reliability of the control.