Model based predictive control is a powerful control strategy to drive electrical machines. Conventional cascade PI(D)controllers are often used to control speed, torque and current. However, for low inertia machines, achieving a high performance and a wide speed range is far beyond the application conditions for which these controllers are designed for. Using two cascaded PI(D) controllers for speed/current control of a low inertia machine, changes in the speed set-point should be applied slowly in order to avoid stability problems. In this paper a model predictive control algorithm is proposed able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The algorithm is implemented on a SPARTAN 3E 1600 FPGA board and experimental results verify the performance of the proposed algorithm.Index Terms-Model predictive control (MPC), permanent magnet brushless DC machine (BLDC machine), speed control, dynamic stiffness, field programmable gate arrays (FPGA). 0278-0046 (c)

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This paper presents a load torque estimation method for self-sensing brushless DC drives. Torque ripples in brushless DC machines can be reduced using load torque information. This method uses the terminal voltage, the virtual neutral point voltage and the DC-bus current of the machine. The algorithm uses the variation of successive back-EMF samples to estimate the rotor speed. The rotor position is estimated by defining an intermediate function of estimated speed and back-EMF samples. An estimate of acceleration is used to estimate load torque. The load torque information is used for increasing the dynamic stiffness of the drive. The mathematical background is given and discussed and the simulations as well as the experimental results prove the performance of the proposed method.Index Terms-Permanent-magnet brushless DC-machine (BLDC machine), back-EMF zero-crossing, self-sensing control, estimation method 0093-9994 (c)

Abstract-This paper presents a study of axial flux permanent magnet synchronous motor (AFPMSM) drive system. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMCbased control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of IMC controller and SVPWM technique to an Axial Flux PM Motor Drive system.

Abstract-The multilevel inverter is a promising technology compared to two-level inverters in the applications of ac-drives and smart-grid applications. In this paper, a dual-T-type threelevel inverters is used to drive an open-end winding induction machine. The Space-Vector Pulse-Width Modulation is selected as a good-performing control strategy to control the dual-inverter. A comparison between the proposed configuration and the conventional diode clamped converter is included. The proposed drive system is designed and modeled by using Matlab/Simulink. It is shown that the converter gives the same hexagon, wave forms and harmonic spectrum of the five level converter. An optimized switching state selection is used to reduce the converter losses. The advantages and drawbacks of the dual-T-type configuration are discussed. In addition, the harmonic analysis and the loss calculations of the dual-T-type converter are provided and compared to the T-type three-level converter and the conventional five-level diode-clamped-converter.