A Study on Improving of Adaptive Sensorless Control Performance via Funnel Control

Model-based sensorless control, which estimates the position and speed of a motor via mathematical analysis, has been studied using various methods because of its simple formula and adequate performance without additional power consumption. Model-based sensorless control estimates the position and speed of an actual motor by interpreting the error between the values calculated by the observer and obtaining the data from the actual motor as an error due to position and speed. Motor parameters can vary depending on temperature and current. This creates additional errors by the observer, which results in a reduction in sensorless estimation performance. To solve these problems, an online parameter estimation method using the recursive least squares (RLS) algorithm or by creating a parameter lookup table through experiments has been studied. However, these approaches have the disadvantage of requiring overly complex formulas or excessive time and effort. In this paper, we propose a new adjustable model that includes parameter errors and adopt a sensorless method that compensates for additional parameter errors via funnel control. This method is more accurate and stable than the existing methods when parameter errors exist. The stability criterion of the algorithm is based on the theory of hyperstability in nonlinear feedback systems. The effectiveness and verification of the proposed algorithm are achieved through simulations and experiments in MATLAB.