In high-performance control of ac machines through variable frequency drives, the knowledge of machine parameters plays a decisive role. The accuracy with which machine parameters can be known is directly related to the time and effort put in testing and commissioning process. In the ever-demanding industrial environments, the time spent on parameter identification translates into loss of production. To reduce commissioning times, research in this direction has focused on automatizing the identification procedure without loss of accuracy. This paper reviews different lines of research adopted over the past few decades for machine parameter identification. Parameter estimation of ac machines is considered because of their widespread applications from servomechanisms to traction to aviation. The surveyed works include self-commissioning schemes that have become an integral part and salient feature of modern electric drives. This feature enables the drives to automatically identify machine parameters and tune the control loops.

This paper deals with the speed and position estimation for synchronous reluctance motors (SyRMs) and interior permanent-magnet synchronous motors (IPMs). A unified design and analysis framework for a class of back-electromotive-force (back-EMF) -based observers is developed and the links between apparently different estimation methods are brought out. State observers equipped with a speed-adaptation law are shown to be mathematically equivalent to voltage-model-based flux observers equipped with a position-tracking loop. The error signal driving the adaptation law or the tracking loop is presented in a generalized form. Using the framework, a stabilizing gain design is reviewed and detailed design guidelines are given. Selected observer designs are experimentally evaluated using a 6.7-kW SyRM drive and a 2.2-kW IPM drive.

This paper deals with the optimal state reference calculation for synchronous motors having a magnetically salient rotor. A lookup table computation method for the maximum torque-per-ampere locus, maximum torque-per-volt limit, and field-weakening operation is presented. The proposed method can be used during the start up of a drive, after the magnetic model identification. It is computationally efficient enough to be implemented directly in the embedded processor of the drive. When combined with an identification method for the magnetic model, the proposed method enables the plug-and-play startup of an unknown motor. Furthermore, a conventional reference calculation scheme is improved by removing the need for one two-dimensional lookup table. A 6.7-kW synchronous reluctance motor drive is used for experimental validation.

Magnetic saturation characteristics of synchronous reluctance motors (SyRMs), with or without permanent magnets (PMs), are highly nonlinear. These nonlinear effects can be included in the current controller by changing its state variable from the stator current to the stator flux linkage using the known saturation characteristics. A direct discrete-time variant of the flux-linkage based current controller is developed in a state-space framework. If the magnetics are modeled to be linear, the proposed control structure reduces to the standard current controller in this special case. Experimental results on a 6.7-kW SyRM drive demonstrate that the proposed flux-linkage based controller enables a higher closed-loop bandwidth and is more robust against parameter errors, as compared to the standard current controller.

This paper deals with stator-flux-oriented control of permanent-magnet (PM) synchronous motors and synchronous reluctance motors (SyRMs). The variables to be controlled are the stator-flux magnitude and the torque-producing current component, whose references are easy to calculate. However, the dynamics of these variables are nonlinear and coupled, potentially compromising the control performance. We propose an exact input-output feedback linearization structure and a systematic design procedure for the stator-flux-oriented control method in order to improve the control performance. The proposed controller is evaluated by means of experiments using a 6.7-kW SyRM drive and a 2.2-kW interior PM synchronous motor drive.

In the control of electric drives, inaccurate estimation of the motor parameters affects the robustness of the control. This effect is observed particularly when the linear lumped parameter-based models are employed to represent a machine composed of nonlinear magnetic materials. Finite element (FE) models consider the material nonlinearity accurately. However, implementing these models in a real-time system is challenging due to the computational burden. In this paper, we propose a fast-dynamic model, based on a model order reduction method, to control a permanent magnet synchronous machine in a wide range of speed. The stator currents in the rotor frame of reference are given as inputs to a reduced FE model, which computes the nodal values of the magnetic vector potential and thereafter the flux linkages. A discrete-time model is used to control the rotor speed and the stator current components. Experiments on a 2.2 kW interior permanent magnet synchronous machine verify the viability of the proposed model.