Mikail Koc

Self-Learning MTPA Control of Interior Permanent-Magnet Synchronous Machine Drives Based on Virtual Signal Injection

This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control (SLC) scheme generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system.

An Inverter Nonlinearity-Independent Flux Observer for Direct Torque-Controlled High-Performance Interior Permanent Magnet Brushless AC Drives

This paper introduces a novel flux observer for direct torque controlled interior permanent magnet brushless AC (IPM-BLAC) drives over a wide speed range including standstill. The observer takes machine nonlinearities into account and is independent of inverter nonlinearities, dead time, and armature resistance variation at steady states since such inaccuracies are compensated quickly by measured phase currents. Magnetic saturations in the stator and rotor cores, cross-coupling effects of flux linkages of the motor, and spatial harmonics in the magnetomotive force are all considered in the novel scheme. There is no filter; hence, no delays and oscillatory responses like in conventional schemes where filters are employed to prevent integrator drift issue. Superiority of the observer when compared to the state-of-the-art schemes has been illustrated by both extensive simulations and experimental results of a 10-kW IPM-BLAC machine designed for traction applications.

Virtual Signal Injection-Based Direct Flux Vector Control of IPMSM Drives

This paper describes a novel virtual signal injection-based direct flux vector control for the maximum torque per ampere (MTPA) operation of the interior permanent magnet synchronous motor (IPMSM) in the constant torque region. The proposed method virtually injects a small high-frequency current angle signal for tracking the optimal flux amplitude of the MTPA operation. This control scheme is not affected by the accuracy of the flux observer and is independent of machine parameters in tracking the MTPA points and will not cause additional iron loss, copper loss, and torque ripple as a result of real signal injection. Moreover, by employing a bandpass filter with a narrow frequency range the proposed control scheme is also robust to current and voltage harmonics, and load torque disturbances. The proposed method is verified by simulations and experiments under various operating conditions on a prototype IPMSM drive system.

Self-learning MTPA control of interior permanent magnet synchronous machine drives based on virtual signal injection

This paper describes a novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control scheme (SLC) generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations under various operation conditions on a prototype IPMSM drive system.

MTPA Control of IPMSM Drives Based on Virtual Signal Injection Considering Machine Parameter Variations

Due to parameter variations with stator currents, the derivatives of machine parameters with respect to current angle or d-axis current are not zero. However, these derivative terms are ignored by most of mathematical model based efficiency optimized control schemes. Therefore, even though the accurate machine parameters are known, these control schemes cannot calculate the accurate efficiency optimized operation points. In this paper, the influence of these derivative terms on maximum torque per ampere (MTPA) control is analyzed and a method to take into account these derivative terms for MTPA operation is proposed based on the recently reported virtual signal injection control (VSIC) method for interior permanent magnet synchronous machine (IPMSM) drives. The proposed control method is demonstrated by both simulations and experiments under various operating conditions on prototype IPMSM drive systems.

Self-Learning Direct Flux Vector Control of Interior Permanent-Magnet Machine Drives

This paper proposes a novel self-learning control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve the maximum-torque-per-ampere (MTPA) operation in the constant-torque region and voltage-constraint MTPA (VCMTPA) operation in the field-weakening region. The proposed self-learning control (SLC) scheme is based on the newly reported virtual-signal-injection-aided direct flux vector control. However, other searching-based optimal control schemes in the flux–torque (f–t) reference frame are also possible. Initially, the reference flux amplitudes for MTPA operations are tracked by virtual signal injection and the data are used by the proposed SLC scheme to train the reference flux map online. After training, the proposed control scheme generates the optimal reference flux amplitude with fast dynamic response. The proposed control scheme can achieve MTPA or VCMTPA control fast and accurately without accurate prior knowledge of machine parameters and can adapt to machine parameter changes during operation. The proposed control scheme is verified by experiments under various operation conditions on a prototype 10 kW IPMSM drive.