Motor control in aerospace, optimizing availability and acoustics

The objective of this research project was to investigate motor control methods applied to Permanent Magnet Synchronous Motors (PMSMs) for aerospace applications. In specific this research attempted to address two key issues that are critical in aerospace. Firstly the increase in system availability in case of a resolver failure by means of applying sensorless motor control methods. Secondly the reduction of acoustic noise generated from a motor drive. Reliability, availability and acoustics are key areas in a number of industries especially aerospace. With regards to the reliability and availability objective, a hybrid model/saliency based sensorless method was investigated that can take over motor control in case of a resolver failure. With regards to the objective on acoustics, the research attempted firstly to address the problem of acoustic noise from High Frequency Injection (HFI). A variant of the Pseudo Random High Frequency Injection (PRHFI) algorithm was thus developed aiming to reduce the perception of acoustic noise. While investigating HFI sensorless methods and observing their acoustic effects, the most novel contribution of this research was conceived. The concept of Active Noise Cancellation/Control (ANC) by means of High Frequency Injection (HFI) was thus created, implemented and presented in this thesis. The proposed availability and acoustic improvement algorithms were first simulated in Matlab/Modelsim and then tested on the Helicopter Electro-Mechanical Actuation System (HEMAS). The above hardware platform is a PMSM based drive used to control the swash-plate onboard a helicopter. The reliability enhancement sensorless observer was demonstrated successfully during testing and was shown to track the motor’s speed and angle. The acoustic suppression algorithms (Pseudo Random High Frequency Injection and High Frequency Injection Active Noise Cancellation) were also demonstrated successfully on the hardware platform by means of audio capturing using microphones and analysis within Matlab

Motor control in aerospace, optimizing availability and acoustics

The objective of this research project was to investigate motor control methods applied to Permanent Magnet Synchronous Motors (PMSMs) for aerospace applications. In specific this research attempted to address two key issues that are critical in aerospace. Firstly the increase in system availability in case of a resolver failure by means of applying sensorless motor control methods. Secondly the reduction of acoustic noise generated from a motor drive. Reliability, availability and acoustics are key areas in a number of industries especially aerospace. With regards to the reliability and availability objective, a hybrid model/saliency based sensorless method was investigated that can take over motor control in case of a resolver failure. With regards to the objective on acoustics, the research attempted firstly to address the problem of acoustic noise from High Frequency Injection (HFI). A variant of the Pseudo Random High Frequency Injection (PRHFI) algorithm was thus developed aiming to reduce the perception of acoustic noise. While investigating HFI sensorless methods and observing their acoustic effects, the most novel contribution of this research was conceived. The concept of Active Noise Cancellation/Control (ANC) by means of High Frequency Injection (HFI) was thus created, implemented and presented in this thesis. The proposed availability and acoustic improvement algorithms were first simulated in Matlab/Modelsim and then tested on the Helicopter Electro-Mechanical Actuation System (HEMAS). The above hardware platform is a PMSM based drive used to control the swash-plate onboard a helicopter. The reliability enhancement sensorless observer was demonstrated successfully during testing and was shown to track the motor’s speed and angle. The acoustic suppression algorithms (Pseudo Random High Frequency Injection and High Frequency Injection Active Noise Cancellation) were also demonstrated successfully on the hardware platform by means of audio capturing using microphones and analysis within Matlab