In order to get rid of the chattering problem of a sliding mode observer, this article presents an adaptive sliding mode observer (ASMO)-based sensorless control for surface-mounted permanent-magnet synchronous machines (SPMSMs). In the conventional sliding mode observer, a large gain is required to ensure the sliding motion stability. However, it leads to undesired chattering and degrades the position estimation accuracy. With the proposed ASMO strategy, the observer gain can be dynamically adjusted according to the back electromotive force (EMF), and it is applicable in wide speed range with improved estimation accuracy. Then, a dual-phase-locked loop (DPLL) is improved to extract accurate rotor position information under dynamic process in both positive and negative rotating directions of the SPMSM. Finally, the effectiveness of the proposed method has been verified by simulation and experiment on a Higale platform with a 0.2-kW SPMSM.

This paper proposes a finite control set model predictive torque control (PTC) of induction machine with an adaptive observer. Model reference adaptive system (MRAS) can accurately estimate the rotor speed. However, the encoderless PTC method requires not only the estimated rotor speed but also the estimated fluxes. A sliding mode stator voltage model observer is applied as the reference model in the MRAS. Although sliding mode method has chattering problem, it can be largely reduced by optimizing the sliding function and by proper sliding gains designed with H infinity method. The proposed encoderless PTC is experimentally verified in this work. The results show that the method is stable in a wide speed range and has good performance over load disturbance. Moreover, the proposed encoderless strategy has the merits of predictive control: fast dynamics, straightforward structure, and easy implementation for constraints’ inclusion.

An encoderless predictive torque control (PTC) is proposed in this paper. By using a rotor flux model reference adaptive system (MRAS) estimation method to PTC, the system has the virtue of low cost due to the absence of PWM and speed measurement components. PTC requires not only estimated speed but also estimated stator and rotor flux. In implementation, a compensated MRAS is considered for obtaining good flux estimations. The experimental results confirm that this control strategy has very fast dynamics, can adapt a very wide speed range, and shows good performance both at transient and steady states.

A field-circuit-coupled parameter adaptive modeling method for a permanent magnet synchronous motor is proposed in this paper. The model combines the merits of a mathematical model and a magnetic field model. It takes into consideration the magnetic saturation, current harmonics, cross coupling, eddy current, and hysteresis loss effect in terms of the corresponding adaptive parameters obtained from the JMAG-finite element analysis (JMAG-FEA) tool in the model. To validate the effectiveness of the proposed model, three modeling methods are comparatively studied through an offline simulation. In addition, a hardware-in-the-loop experiment platform is built combining one DSP-based controller and a dSPACE-based controller, where the three models are located. The results of both, the simulation and HIL experiment, demonstrate the accuracy of the proposed model and the feasibility of the integration scheme of motor design and control. Moreover, the proposed model approximates the actual machine well and can be customized conveniently according to different requirements.

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is a high-performance drive machine and has been adopted in many applications. The difficulty of implementing its sensorless control at low and zero speeds is its multiple saliencies, which is much more significant than most other ac machines. The traditional decoupling methods provide successful results only under the condition that high-order saliencies are not stronger than half of the primary saliency. Furthermore, the behavior of the multiple saliencies is principally frequency dependent. Based on the characteristics of such machines, this paper proposes a multisignal injection method for realizing sensorless control. This method injects multiple high-frequency signals with different frequencies and magnitudes into the machine. Different frequency components in the response current signals are demodulated and then combined together to get the clear primary saliency signal, which is used to identify the rotor position. This new method was validated using a cwSPMSM at low speed. The experimental results proved the effectiveness and accuracy of the new method.