Magno Ayala

High-performance sensorless operation in multiphase electric drives requires speed estimation techniques capable of providing fast dynamic response, reduced oscillatory behavior, and low implementation complexity. In this context, a sliding-mode observer (SMO) based on an exponential reaching law (ERL) is proposed for rotor speed estimation in asymmetrical six-phase induction machines operating under indirect rotor field-oriented control. Unlike conventional SMO implementations, the proposed approach avoids auxiliary low-pass filtering (LPF) stages by employing an ERL-based adaptive gain mechanism, thereby preventing the phase delay and bandwidth reduction commonly associated with LPF-based observers. As a result, the proposed observer preserves fast transient dynamics, attenuates chattering near the sliding surface, and improves the smoothness of the estimated signals. The proposed technique is particularly suitable for multiphase drive applications, where sensorless operation reduces hardware complexity and improves system reliability by eliminating mechanical speed sensors and associated wiring. A Lyapunov-based stability analysis is presented to demonstrate the convergence properties of the observer and discuss the influence of the ERL parameters on the estimation dynamics. Simulation and experimental results obtained on a real-time test bench validate the digital implementation of the proposed SMO + ERL, demonstrating improved transient tracking, smoother estimated signals, stable low-speed operation, satisfactory speed reversal performance, and effective operation under loaded conditions.

This paper addresses the regulation of stator currents in a six-phase asymmetrical induction machine during field-weakening operation, accounting for disturbances and parameter variations. Current control is performed in the d–q, α–β, and x–y reference planes. A novel double-loop current controller based on a discrete-time super-twisting algorithm is proposed to enhance current regulation, particularly under field-weakening conditions where current tracking errors typically deteriorate. This controller ensures precise current tracking with fast error convergence and improved robustness to uncertainties. Experimental results confirm the effectiveness of the controller under both transient and steady-state conditions, including during the field weakening operation.

Finite-control-set model predictive control (FCS-MPC) has become widely accepted in multiphase drives due to its adaptability and robust dynamic response. However, multiphase machines, characterized by low secondary plane ($x-y$) impedance, which is absent in their three-phase counterparts, often exhibit poor current quality under conventional FCS-MPC strategies. This paper proposes a novel predictive torque control (PTC) scheme based on virtual vectors to improve ($x-y$) current regulation by ensuring zero average ($x-y$) voltage generation. The proposed method is compared with classic PTC, which cannot achieve proper ($x-y$) current regulation because it uses a single switching state during the sampling period. Experimental validation includes a detailed comparison of torque and flux behavior, ($x-y$) current mean square error, and total harmonic distortion of the fundamental stator currents, demonstrating the effectiveness of the proposed control approach under various operating conditions. Experimental results confirm reductions of approximately 79.7% in computational burden and 98.4% in copper losses when using PTC-VV compared to classic PTC. An asymmetrical six-phase induction machine is used as the case study.

This paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness and accuracy of the developed sensorless scheme are verified by experimental results, which demonstrate the system’s performance under various operating conditions. These results demonstrate the advantages of the proposal as a valid alternative to the conventional method, which uses a mechanical speed sensor for multiphase machines. Additionally, the sensorless approach can also serve as a redundant backup in the event of mechanical sensor failure, thereby increasing the reliability of the overall drive system.

Model predictive control has become a powerful and versatile control strategy, particularly effective in controlling multiphase machines. One of its key advantages is the ability to address multiple control objectives by incorporating constraints directly into the cost function. However, this feature typically necessitates the challenging task of tuning weighting factors to balance competing objectives effectively. This paper proposes a sequential model predictive torque control strategy for six-phase induction machines, eliminating the need for weighting factors. The proposed approach simplifies the control design process while maintaining high performance. Simulation and experimental results demonstrate the method's effectiveness, showing accurate torque, flux tracking, and appropriate stator currents regulation in the $\alpha -\beta$ and $x-y$ planes. These results highlight the potential of the proposed control strategy for practical implementation in advanced multiphase drive systems.

This paper presents a model predictive control (MPC) strategy to regulate output voltages in a multi-modular matrix converter topology for isolated loads. The converter system harnesses power from a six-phase permanent magnet synchronous generator (PMSG) to deliver sinusoidal voltages to a three-phase load, with LC filters positioned at the output of each MC module within the multi-modular scheme. The proposed MPC approach ensures that the output voltages remain within acceptable ranges of magnitude, phase, and frequency, even under load variations and system faults. This control strategy is particularly suitable for uninterruptible power supply systems, microgrids or other applications where voltage regulation is critical. Experimental studies validate the effectiveness of the control strategy under various load conditions, reference voltage changes, and simulated system fault scenarios. The results highlight the robustness and reliability of the proposed voltage control using the multi-modular matrix converter.

Finite-control-set model predictive control (FCS-MPC) has achieved superiority in managing multiphase induction machines due to its quick dynamic response, control flexibility, and overall good performance. Its advantages, including simplicity, computational efficiency, compensation for system perturbations, and effective handling of multivariable problems, have made it a competitive alternative in various industrial applications. Nevertheless, FCS-MPC has some limitations. It is highly dependent on the accuracy of the predictive model’s parameters. Unfortunately, the estimation of magnetizing inductance, the most critical factor, especially in the weakening field region, has not been studied yet. Focusing on this gap, this article proposes a technique to estimate the magnetizing inductance in the field weakening zone for a six-phase asymmetrical induction machine driven by the FCS-MPC. Experimental tests have verified the effectiveness of the proposed method, considering stator currents and rotor speed tracking, as well as a reduction in the (x − y) currents.