Position sensorless adaptive positioning servo system based on DyCE principle with adaptive control input synthesis using convolutional integration for differential calculation

This paper proposes an adaptive positioning control of a servo system using a position sensorless controlled interior permanent magnet synchronous motor (IPMSM), in which the proposed adaptive system is based on Dynamic Certainty Equivalence (DyCE) principle using the convolutional integration for differential calculation. Generally, the adaptive control system requires strictly positive real (SPR) property for a certain transfer function of the control object for stable parameter identification. This controlled system has three relative degree, however this paper applies DyCE principle to reduce the relative degree, and to realize the stable parameter identification. In this approach, the control input synthesis requires the second-order differential calculation of the adjustable parameters of the controlled object. This paper also proposes the second-order differential calculation of the identified controller parameters using the convolutional integration for the control input synthesis. Finally, this paper also demonstrates the feasibility of the proposed method by some experimental results.