Search

To achieve high efficiency and dynamics in electric drive applications, it is necessary to have accurate torque control. This is typically accomplished through a current regulator that is fed by references generated by various open-loop control strategies, in order to obtain the desired torque. As an alternative, this work presents a model predictive torque control. Starting from the torque reference, the algorithm generates optimal voltage references to the inverter-fed synchronous motor drive, while working at maximum efficiency and considering the motor current limit. This feature is achieved by combining two different norms in the cost function. According to the paradigm of the more autonomous drives, an important feature is that the algorithm requires only knowledge of the motor model. This means that a tuning procedure for control weight is no longer required as analytically discussed in this work. Experimental validation of the proposed technique are performed on a test rig featuring an anisotropic permanent magnet motor in different dynamic operation, including flipping from the motor nominal working point to the generator one.

The work focuses on a new mathematical formulation for the transfer function of a pulsating injection-based position observer, as the key point for its correct design. The proposed analysis exploits the modulation/demodulation control theory to derive the transfer function of the whole estimator in the Laplace domain. Magnetic iron saturation effects and cross–saturation between the dq-axes are taken into consideration in the observer design. To test the effectiveness of the new transfer function representation of the observer, it is used to synthesise two different regulators, namely by the direct synthesis and the internal model principle. An extensive experimental stage is included pointing to fully verify the accuracy of the proposed model and the theoretical considerations. A comparison with the existing mathematical model of the observer is also reported to show the improvement achievable in the position estimation by the proposed observer synthesis.

By applying rotor angle sensorless control methods the costs of the electrical drive can be decreased while its reliability is increased. Traditionally, the design of both the rotor angle estimator and the drive controller require a detailed motor model and manual tuning leading to significant effort by human experts. In this work, a rotor anisotropy-based sensorless control scheme for interior permanent magnet synchronous motors (IPMSM) is proposed that enables sensorless control with minimum tuning effort and a priori motor knowledge. Due to these characteristics the scheme is particularly suitable for self-commissioning or low-cost drive applications. The scheme contains a finite control set model predictive current controller (FCS-MPCC) with an additional inequality constraint that enables the identification of the motor model in the stator-fixed coordinate system by using the last three measurement samples. Utilizing an automatic system identification procedure, the motor model is determined online in a data-driven fashion. The identified motor model serves both as prediction model of the FCS-MPCC and as baseline for the rotor angle estimation via an eigenvalue decomposition approach. Challenging experimental investigations at standstill up to the medium speed range prove the applicability of the proposed approach. Here, highly dynamic speed and current transients can be handled by the proposed method.

This article deals with a finite-set model predictive current control in synchronous motor drives. The peculiarity is that it does not require the knowledge of any motor parameter. The inherent advantage of this method is that the control is self-adapting to any synchronous motor, thus easing the matching between motor and inverter coming from different manufacturers. Overcoming the flaws of the existing lookup table based parameter-free techniques, the article elaborates the past current measurements by a recursive least-square algorithm to estimate the future behavior of the current in response to a finite set of voltage vectors. The article goes through the mathematical basis of the algorithm till a complete set of experiments that prove the feasibility and the advantages of the proposed technique.

The article deals with the flux linkages estimation of a synchronous motor during either speed or load transients. The nonlinear magnetic model is obtained by a radial basis function neural network (NN), which yields a differentiable function as link among currents and flux linkages. As a distinctive feature, the training of the NN is performed by an innovative algorithm that operates both at steady state and during speed and load transients. The method has been validated experimentally by several tests performed on different synchronous reluctance and interior permanent magnet motor drives.

The performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors.

The high-frequency signals normally used for sensorless operations in anisotropic permanent-magnet synchronous motors cause unwanted torque ripple, noise, and vibrations. The target of the present research is to extend downwards the speed range in which the sensorless drive can work without any signal injection. Specifically, this paper proposes a new back-electromotive force observer that eliminates the instability that may arise in conventional observers when the motor drive operates in generating mode. Two different versions of the observer are proposed and discussed, to include motors with high harmonic content in the back-electromotive force. The detailed mathematical analysis of the proposed observer is supported by simulations and the practical feasibility is proven through experimental results.

The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems.

This paper shows how the problem of estimating the initial rotor position in permanent-magnet sensorless drives can benefit from the availability of a direct measurement of motor phase voltages. Two different estimation methods are presented for this purpose. The first is a readjustment of a classic procedure based on the detection, by means of the injection of voltage test pulses, of inductance variations due to motor saliencies, from which it is then possible to infer the position of the rotor. Since the application of irregular test pulses may increase the estimation uncertainties, the available voltage measurement is exploited to implement a closed-loop amplitude control of the test pulses. The second method is introduced both to overcome an issue related to the digital measurement approach adopted in this paper and to allow position estimation even in a motor with no relevant saliency, a case for which the first method is inappropriate. The effectiveness of the proposed solutions is validated by several experimental tests, which are carried out on two motors with different saliency properties.

In modern electrical drives, the energy saving starts with the careful generation of the electromagnetic torque, which involves the electrical motor and its current control. A modern choice is represented by an anisotropic synchronous interior permanent magnet (IPM) motor combined with the maximum torque per ampere (MTPA) control technique. The MTPA locus brings along additional information that is worth deepening. In particular, this paper shows that a proper rearrangement of the torque expression in MTPA condition, merged with a simple least-square algorithm, returns an accurate online estimation of the electromagnetic torque. The effectiveness of the proposed method is proved by a worked out example, based on an IPM drive model fitted with finite-element analysis and experimental measurements, including motor saturation effects. The torque estimator extends its usability in the whole working region of the drive, even during transients outside the MTPA curve, and it comes almost for free. In this, the proposed method should be technically sound in many industrial applications.

This paper presents a theoretical analysis of a maximum-torque-per-ampere control strategy based on the extremum seeking control working principle, which provides useful insights for the definition of a systematic and quantitative design procedure. The focus is on the convergence properties to the optimal operating point, and a method for evaluating an upper bound of the convergence time is proposed. The analysis is supported by several experimental tests performed on an interior permanent-magnet synchronous motor.

The paper deals with the torque-ripple reduction in a permanent magnet synchronous motor (PMSM) drive with distorted back electromotive force. A smooth torque is obtained by tracking a modified current reference which is periodic over one-sixth of the electrical time period in the synchronous reference frame. An accurate tracking involves, however, very high current loop bandwidth, which is usually not achievable with conventional linear controllers. In order to improve current tracking in the presence of periodic reference signals and disturbances, the paper proposes the application of repetitive techniques to the current control in a field-oriented PMSM drive, where the q-axis current reference has been modified to achieve constant torque. The paper investigates the advantages and pitfalls of the method, through a mathematical analysis and an experimental validation obtained on a laboratory prototype. Particular emphasis is placed on the adjustments that have been specifically studied to enhance the overall system performance.

The paper deals with the experimental realisation of a sensorless interior permanent magnet (IPM) synchronous motor drive. The motor has been specifically designed to get a wide flux-weakening region, to minimise the drive power rating in spite of a wide speed range. Position and angular speed of the rotor are obtained through an extended Kalman filter (EKF). The estimation algorithm requires neither the knowledge of the mechanical parameters, nor the initial rotor position. In the paper, particular emphasis is placed on control algorithms, that are complicated by the motor anisotropy and that have been specifically studied to enhance the overall system performance.

This paper concerns the realization of a sensorless permanent magnet (PM) synchronous motor drive. Position and angular speed of the rotor are obtained through an extended Kalman filter. The estimation algorithm does not require either the knowledge of the mechanical parameters or the initial rotor position, overcoming two of the main drawbacks of other estimation techniques. The drive also incorporates a digital d-q current control, which can be easily tuned with locked rotor. The experimental setup includes a PM synchronous motor, a pulsewidth modulation voltage-source inverter, and floating-point digital-signal-processor-based control system.