Sensorless Motor Position Estimation With Convexity-Based Filtering
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Solution Overview
Problem
Synchronous motor drives require accurate rotor position and speed information for high-performance control, but existing position/speed sensorless estimation schemes struggle with achieving robust and reliable estimates due to the nonlinearity of the problem statement.
Innovation Solution
The proposed solution involves a high-performance filtering technique for direct position and speed estimation in electric motor drives. This technique includes an optimization process to derive instantaneous estimates, followed by the application of various filters such as selective filtering, FIR filtering, dual PLL filtering, and the use of perturbation signals to improve robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If position sensorless estimation schemes are used to remove physical sensors, then reliability and cost are improved, but measurement precision and robustness deteriorate due to nonlinearity
Solution Approach 1:
The patent transforms the nonlinear sensorless estimation problem into a linear optimization framework by changing the mathematical parameters and formulation. The cost function is designed to be convex and differentiable, allowing standard optimization techniques to achieve accurate position and speed estimates without physical sensors, thus maintaining both reliability and measurement precision.
Solution Approach 2:
The patent introduces an optimization-based intermediary layer that processes motor current and voltage signals to derive position and speed estimates. This intermediary optimization framework acts as a mediator between the sensorless estimation requirement and the need for accurate measurements, resolving the contradiction by providing a mathematical transformation mechanism.
2Productivity
If optimization-based direct estimation is used to achieve independent position and speed estimates, then control performance is improved, but robustness against disturbances deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the optimization process continuously adjusts position and speed estimates based on motor terminal measurements. The cost function incorporates feedback from current and voltage signals, allowing the system to maintain high control performance while compensating for disturbances through continuous optimization and correction.
3Reliability
If filtering techniques are applied to improve estimate robustness, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges the filtering function into the optimization process itself. Rather than adding separate filtering stages, the cost function is designed to inherently filter out disturbances through its mathematical structure, combining estimation and filtering into a single unified optimization framework that reduces overall system complexity.
Solution Approach 2:
The optimization-based estimation framework serves multiple functions simultaneously: it provides position estimation, speed estimation, and disturbance rejection filtering all through a single mathematical process. This multi-functionality eliminates the need for separate dedicated filtering components, improving robustness without proportionally increasing complexity.
Data Source
AI summary
Disclosed are implementations, including a method that includes obtaining measurement samples relating to electrical operation of an electric motor drive providing power to an electric motor, deriving, based on the samples, instantaneous estimates for parameters characterizing speed and/or position of the motor according to an optimization process based on a cost function defined for the samples, and applying a filtering operation to the instantaneous estimates to generate filtered values of the motor's speed and/or position. The filtering operation includes computing the filtered values using the derived instantaneous estimates in response to a determination that a computed convexity of the cost function is greater than or equal to a convexity threshold value, and/or applying a least-squares filtering operation to the derived instantaneous estimates and using at least one set of previous estimates derived according to the optimization process applied to previous measurement samples.