In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

This paper presents a model-free predictive current controller for permanent magnet synchronous motors. The dynamic model of the motor currents is represented using an ultra-local model, where all system parameters, nonlinear terms, and other unmodeled dynamics are encapsulated into two lumped functions. These functions are estimated online using a second-order fixed-time sliding mode observer, allowing for system model estimation without requiring exact knowledge of the system parameters in advance. Subsequently, a predictive controller is designed by taking into consideration the inherent one-step delay. Simulation results are presented to showcase the effectiveness and efficacy of the proposed approach.

This paper analyzes the sensitivity of the rotor parameter estimation of an advanced$\Gamma-\mathbf{model}$for multiphase induction machine. These machines exhibit inter-plane cross saturation and certain sub-topologies, such as variable phase-pole machines, may operate briefly with high rotor frequencies, which warrants the use of such models. It is shown that accurate non-linearity compensation of the voltage vector and characterization of the drive's input/output-delays is paramount for the rotor parameter estimation. Moreover, standstill experiments using a simple inverter excitation signal are designed to improve on the offline parameter estimation by means of a prediction error method. The presented method may find usage in (self-) commissioning of multiphase induction machines.

A special subcategory of multiphase electrical ma-chines are the so called variable phase-pole machines. They have the ability to electronically change the number of pole- pairs without any hardware reconfiguration. Moreover, they are inherently fault tolerant due to the higher number of independent windings. These two virtues offer new possibilities for a drivetrain with higher power density and a new level of reliability. This paper presents an adapted post-fault controller performing a pole transition with an open-phase fault. The drive changes the number of pole-pairs without loosing the torque during the transition and minimizes the stator copper losses simultaneously. The validity of the presented control is shown in simulations.

Multiphase electrical machines have the inherentadvantage of fault tolerance as compared to conventional threephasemachines. A special category of multiphase electricalmachines are variable phase-pole machines, which allow a changebetween phase-pole configurations without hardware reconfiguration,but merely through current control. This paper proposesan open-fault detection method for variable phase-pole machines; fast, non-invasive, parameter- and operation-independent. Themethod analyzes the space harmonics of the current distributionusing the harmonic plane decomposition theory, which isalready used to perform a seamless switch between phase-poleconfigurations. Simulation results demonstrate the feasibility ofthe approach.

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.

In this paper the harmonic plane decomposition from part I is completed. Multiphase electrical machines with independently controlled stator coils benefit from a fixed amount of current controllers and unified models. In part I, the foundation for such models was laid. This paper shows that the choice of the base case has to fulfill certain criteria to properly represent vector-space quantities. More specifically, phase-pole configurations with an even number of pole pairs need adapted base cases with reduced rank as compared to the odd base case. Continuous modeling and control requires retention of the dimensionality of Clarke matrices. A different number of stator and rotor slots can lead to aliasing in the transformation, when keeping the dimension consistent. Therefore, a specific Clarke transformation for rotor quantities based on the developed theory is elaborated. Finally, it is demonstrated how the harmonic plane decomposition can be used to model each harmonic plane of an arbitrary phase-pole configuration with conventional machine equivalent circuits, e.g. the T-equivalent or inverse-Γ models.

In this first paper of a two-part series, the harmonic plane decomposition is introduced, which is an extension of the vector-space decomposition. In multiphase electrical machines with variable phase-pole configurations, the vector-space decomposition leads to a varying numbers of vector spaces when changing the configuration. Consequently, the model and current control become discontinuous. The method in this paper is based on samples of each single slot currents, similarly to a discrete Fourier transformation in the space domain that accounts for the winding configuration. It unifies the Clarke transformation for all possible phase-pole configurations such that a fixed number of orthogonal harmonic planes are created, which facilitates the current control during reconfigurations. The presented method is not only limited to the modeling of multiphase electrical machines but all kinds of existing machines can be modeled. In the second part of this series, the harmonic plane decomposition will be completed for all types of machine configurations.

The emulation of mechanical systems by means of electrical systems is an application of electrical machines and drives which have received increased attention in the recent years. As power electronics-controlled machines are dynamically faster than conventional mechanical systems, it is widely recognised that the behaviour of the latter can be emulated by a proper electrical machine torque control. Different test benches with different emulation capabilities have been proposed in the literature, but few attention has been posed to two aspects: the requirements of the drive emulator in terms of rated torque and/or power and/or bandwidth, and the emulation of effects like mechanical resonances within test benches with rigid mechanical connection. The paper tries to focus on these two aspects with both simulation and experimental results, in order to depict a general approach to the design of a drive emulator.