Full-order adaptive observer for interior permanent-magnet synchronous motor based on novel fast super-twisting algorithm

To improve the accuracy of speed estimation strategy over a wide speed range in the sensorless speed control system of an interior permanent - magnet synchronous motor (IPMSM), a full-order adaptive observer based on a novel fast super-twisting algorithm (NFSTA-AO) is proposed. The conventional model reference adaptive system (MRAS) takes the linear compensation matrix of the unit matrix, which can only achieve speed discrimination within a certain speed range. Therefore, in this paper, a new linear compensation matrix is first derived using Popov's super stability theory, and then, a new tachograph - adaptive law is obtained. A feedback correction link is also added to the adjustable model to improve the convergence speed of the error between the reference and adjustable model outputs. To further improve the accuracy of the tachograph estimation strategy, a novel fast super-twisting algorithm (NFSTA) convergence law is introduced in place of the adaptive law in the full-order adaptive observer, combining the advantages of both algorithms. The NFSTA added the inverse hyperbolic sine function based on system state variables to the integral term of the fast super-twisting algorithm (FSTA) to effectively suppress the torque pulsation of the system. A soft switching function is also designed to replace the symbolic switching function in order to reduce the system jitter caused by the sliding mode variable structure control. The simulation experiments show that the system using the NFSTA-AO estimation strategy is more resistant to disturbances and robust; additionally, it has better dynamic following than the conventional MRAS in the presence of added load disturbances and sudden speed changes.