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Removing some sections of the stator yoke in a permanent-magnet brushless machine can be beneficial for reducing punching waste and simplifying motor manufacture. However, in some cases, restricting the possible flux paths, in this way, will have a detrimental impact on the torque and air-gap harmonics. This paper discusses the potential implications of modular stator core arrangements and presents the air-gap flux density harmonics and winding factors for potential slot/pole combinations. Finite-element analysis (FEA) simulations are presented to support the analytical calculations. The analysis indicates that the performance reduction from using a modular stator is minimal when the number of slots and poles are similar but drops off substantially when this is not the case. A modular core stator will increase the MMF subharmonics due to the magnet field but can reduce the subharmonics due to the armature if a single-layer winding is used. The effect of slotting is very similar for a modular and conventional core machine, and FEA results match previously published analytical analyses.

Cogging torque in fractional pitch PMSM is minimized when manufacturing induced errors are also minimized. This paper investigates additional cogging torque harmonics resulting from misalignment of the rotor and stator caused by assembly and manufacturing tolerances in an axial flux, fractional pitch PMSM. A hybrid analytical / FEA method utilizing superposition of a pole transition over a single stator slot is developed to predict the effect on cogging torque of angular misalignment in combination with pole and slot placement inaccuracies. This work extends on that previously published by inducing varying degrees of assembly misalignment of the stator and rotor to determine the effect on cogging torque waveforms. Experimental data is presented supporting the analytical data indicating that static angular misalignment produces sidebands about the rotor affected harmonics and dynamic angular misalignment creates sidebands around the stator affected harmonic. There is good agreement between the analytical method, FEA and measured experimental data.

Removing some sections of the stator yoke in a permanent magnet brushless machine can be beneficial for reducing punching waste and simplifying motor manufacture. However in some cases, restricting the possible flux paths in this way will have a detrimental impact on the torque and air-gap harmonics. This paper discusses the potential implications of modular stator core arrangements and presents the air-gap flux density harmonics and winding factors for potential slot/pole combinations. FEA simulations are presented to support the analytical calculations. The analysis suggests that the performance reduction from using a modular stator is minimal when the number of slots and poles are similar but drops off substantially when this is not the case. A modular core stator will increase the MMF sub-harmonics due to the magnet field but can reduce the sub-harmonics due to the armature if a single layer winding is used. The effect of slotting is very similar for a modular and conventional core machine and FEA results match previously published analytical analyses.

A major issue for mass produced permanent magnet synchronous motors (PMSM) is the minimization of acoustic noise. A significant source of this noise is cogging torque. Design related cogging torque that does not vary between motors can be compensated by a predetermined control scheme. However cogging torque caused by variation in the manufacturing process will vary from motor to motor and thus needs to be compensated online on a motor by motor basis. While schemes have been published for cogging torque compensation for motors with encoders, to date no schemes have been proposed for use with sensorless drives (without position sensors). As sensorless drives make up a large proportion of the mass produced PMSM market, online estimation and compensation of cogging torque without position feedback deserves investigation. The success of a compensation scheme will be based on the accuracy of the cogging torque estimation. This paper presents a scheme for estimating the dominant cogging torque harmonics using a back-EMF estimator sensorless controller. Experimental results show that over the expected range of system variation in viscous friction, the estimate will vary by less than 20%. Analysis shows that if this estimate was used in an iterative cogging torque cancellation scheme then the critical harmonics would be reduced to less than 10% in 2 iterations.

Torque ripple exists at the output of permanent magnet synchronous motors (PMSMs) as a result of current error, non-sinusoidal back-EMF and cogging torque. Since part of the cogging torque component is based on manufacturing error, motor design may not eliminate all torque ripple. One possible method for reducing torque ripple is by injecting the inverse of a torque ripple estimate to the current controller. Torque estimators that include cogging torque rely on feedback from different sensors such as vibration sensors and accelerometers, microphones, speed and torque sensors. This paper outlines a control method that can utilize any sensor with a linear transfer function between the torque and the sensor input. The method determines an estimate of the torque ripple for a number of harmonics simultaneously, as well as the transfer function between the torque and the sensor. Experimental results show different sensor types provide similar estimates of the torque with less than 2% variation in magnitude for the sensor types used. Experimental results show that regardless of which sensor type is used, critical torque harmonics are reduced by at least a factor of 3.

Torque ripple minimisation is achieved by an adaptive feedforward control method to reduce cogging torque and current measurement errors. Experiments have been conducted to show that torque ripples are highly sensitive to changes in current offset and cogging torque. An easy way to adapt to cogging torque changes (due to temperature) is proposed and implemented after an initial estimation of the cogging torque is done. This initial estimation is necessary for manufacturing inaccuracies and ongoing adaptation is to cope with operating conditions changes. Higher harmonics of the cogging torque can also be compensated through this way. Current offset which contributes to a large proportion of the torque ripple is also compensated through an adaptive method. The advantages of the proposed scheme combine the benefits of preprogrammed waveform methods which are easy to implement and a simple adaptive system to effectively reduce RMS torque ripple factor from 11.30% to 1.7%.

Stator manufacturing processes for axial flux permanent magnet synchronous motors (AFPMSM) are susceptible to variation leading to cogging torque. To date, there are no methods which synthesize cogging torque based on stator manufacturing errors. This paper presents a superposition method based on stator geometry to predict critical cogging harmonics for AFPMSM stators. Geometry is obtained using image processing techniques on a scanned image of the stator. The proposed method has been validated by comparison with experimental cogging torque data. A correlation of 0.82 linear regression was found using the method proposed in this paper. Process capability was tested by measuring slot widths and checking deviation.

Theoretically, the layout of fractional pitch permanent magnet motors should ensure the cancellation of most cogging torque harmonics. In reality, manufacturing errors lead to incomplete cancellation and higher than expected cogging torque. Two potential manufacturing errors are the variation in angle between successive magnets and the strength of the magnets. To determine the sensitivity of cogging torque to magnet strength and placement, a superposition technique is used. This technique is validated by first demonstrating that the total cogging torque can be reconstructed by summing offset versions of the torque from experimentally determined single-magnet/single- slot interactions. Following this validation, Monte Carlo simulations are conducted to statistically determine the sensitivity of cogging torque to magnet placement and strength errors. It is demonstrated that the mean RMS cogging torque increases by 4 % for every degree increase in magnet placement standard deviation and by 0.06% for every 1% increase in magnet strength standard deviation.

Fractional pitch is commonly used to significantly reduce cogging torque in PMSM, however, maximum benefit is dependent on accurate stator and rotor manufacturing. This paper presents a method of decoupling the stator and rotor contributions to total cogging torque. Rotor causes are further decoupled into magnet placement and strength variation. Decoupling is possible due to stator and rotor affected harmonics being independent of one another. Magnet strength and position decoupling is based on the analysis of the cogging torque waveform generated by the rotor interaction with a single slot stator and utilizes the zero torque produced when a single magnet is directly over a slot. Superposition and least squares minimization is then used to determine strength variation and simulate cogging torque with and without placement and strength variation. Analysis of ten production stators and rotors is presented and discussed, with the overall findings confirming that for the motors tested, the largest contributors to manufacturing induced cogging torque were the stator, magnet placement inaccuracy and magnet strength variation. Eliminating stator variation would improve cogging torque by 45%, perfect magnet placement would result in a 29% reduction in cogging torque and eliminating magnet strength variations would achieve a 7% reduction.

Torque ripple minimisation is achieved by an adaptive feedforward control for mass produced permanent magnet synchronous motors. Torque ripple, a major problem for such motors can be reduced using appropriate estimation scheme from speed information. The proposed method employs online zero phase filtering which can effectively minimise all torque harmonics at low speed operation. Experimental results validate the proposed scheme reducing the torque ripple factor from 6.87% to 1.28% using the proposed method. The adaptation scheme make use of PD type iterative learning control scheme which has the advantage of fast convergence compared to the more commonly used P type iterative learning control. The PD-ILC scheme is also quite robust to an initial error in the estimation of the parameter J.

The Permanent Magnet Synchronous Machine (PMSM) is gaining popularity in many applications due to its high efficiency and high power density. In order to achieve efficient and robust motor operation, motor position information is required. If active compensation of motor torque ripples is to be achieved in addition to simply rotating the motor the quality of the position information becomes even more important. Traditionally this position information is acquired through the use of a position sensor, however so called `sensorless' control schemes are now becoming a popular alternative. This paper presents a review of the sensorless position estimation algorithms which are being adopted by industry. A framework is developed to assess the sensitivity of these algorithms to errors in motor parameters and non-ideal aspects of the motor and controller. This framework is based around a MATLAB®/Simulink® simulation which includes these non-ideal aspects. The simulated control loop is designed to be very close to the control loop implemented on a DSP and used to run a real motor. Results for a simple back-EMF estimator show that a 10-bit or better analog to digital converter (ADC) gives good performance, the position estimation error is almost insensitive to resistance variation and a 20% change in inductance results in a 0.7% offset error in the position estimate.

Feedforward-feedback control can be used to achieve torque ripple minimization or error minimization between the reference and the output. Theoretically, if the inverse transfer function of the plant is known, it is possible to achieve zero error through feedforward control. In this paper, the references (acceleration, velocity and position) are designed so that they can be used as inputs to the feedforward model. A generic feedforwardfeedback control scheme for different back EMF shapes has been designed. It has been shown experimentally that the proposed feedforward scheme works well with the references. An error which is four times smaller is achieved using feedforward-feedback control compared to using feedback control only. Furthermore, the feedforward scheme is simply the inverse transfer function of the motor dynamics of a PM motor. It is simple and not computational intensive unlike most feedforward design. If there are any changes to J and B, it has been simulated successfully that the MRAS estimator is able to adapt accordingly, using the estimated J and B as inputs to the feedforward scheme. This allows a more robust feedforward control as a whole.

Theoretically, if the inverse transfer function of the plant is known, feedforward control can achieve zero error between the reference and the output. The main aim of this paper is to design a simple feedforward model and to compensate for the errors brought about by a change in the cogging torque and current measurement inaccuracies. The reference position, speed and acceleration have been designed so that they can be used as inputs to the feedforward model. The feedforward model is a stable form of the inverse transfer function of the motor dynamics of a PM motor. MRAS technique has been used to compensate for changes to the cogging torque and current measurement inaccuracies. A successful indirect adaptive feedforward control has been designed for three different shapes of the back EMF (sinusoidal, trapezoidal and a mix of the first two shape using real motor back EMF) and the control scheme is validated through hardware experimentation.

Many methods have been published for minimizing pulsating torque using programmed reference current waveforms (PRCW), however there are still several problems with their implementation. One major problem is the need to determine motor and controller parameters very accurately. The task of parameter determination is a considerable challenge as any measurement usually involves a number of conversions or scaling factors. Previous implementations of PRCW methods have either used data-sheet values for this scaling or separate calibrations of each individual scaling factor. The limited success of these implementations suggests that a more accurate method is required for parameter estimation. This paper presents an approach to parameter determination using pulsating torque decoupling (PTD), where the motor parameters are determined from the pulsating torque itself from an initial test. To validate this method, it has been implemented on a test motor. Using traditional methods of motor parameter determination, the pulsating torque was approximately 4%. Using the proposed PTD method, the pulsating torque was reduced to approximately 1%.

One of the disadvantages of Permanent Magnet Synchronous Motors (PMSM) is the output torque ripples which degrade the performance of the system. Actual torque feedback to the system reduces output torque ripple but increases costs through the use of a torque transducer. Thus, a torque estimator which can provide accurate instantaneous torque feedback to the system will be highly desirable. However, there is no previous work comparing the different methods in estimating torque for a PMSM. This paper will compare four torque estimators using MATLAB/Simulink™ to test how well the estimated torques are when compared to the actual output torque. The four criteria used for comparing the performance of the torque estimators are accuracy, robustness, complexity and cost. Depending on the type of operations, the torque estimators have different performance. From the simulations, it is found that if high accuracy and robustness is needed, Model Reference Adaptive System (MRAS) and Sliding Mode Observation (SMO) may be the best choices. However, if low cost and low complexity is desired, General Torque Equation (GTE) and Flux estimation with Compensation Scheme (FCS) should be chosen.