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The performances of a model predictive control (MPC) 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 look-up 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 analysed. 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 general trend towards 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 the 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 FPGAbased control systems.Index Terms-Permanent magnet motors, reluctance motor, artificial neural networks, magnetic flux linkages. NOMENCLATURE Variables names convention:• variable with accent : estimated quantities • bold lowercase variable: vectorial quantities Symbols used:flux linkages τ (t) Electromagnetic torque d,q Voltage estimation errors N g K Number of Gaussian functions b k Proportional coefficient of the first layer d max Diameter of the quadratic training region n k , x k , a k Input, centre in (d, q) reference frame and output of the k th Gaussian functions w d,q k Proportional coefficients of the second layer M Number of steady state training points L. Ortombina, F. Tinazzi and M. Zigliotto are with

(2017) Energy-efficient autonomous solar water-pumping system for permanent-magnet synchronous motors. IEEE Transactions on Industrial Electronics, 64 (1). pp. 43-51. ISSN 1557-9948Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/40003/1/Energy-efficient%20autonomous%20solar%20water-pumping%20system%20for%20permanent-magnet%20synchronous%20motors.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 Energy-efficient autonomous solar water-pumping system for permanent magnet synchronous motors Riccardo Antonello, Member, IEEE, Matteo Carraro, Alessandro Costabeber, Member, IEEE, Fabio Tinazzi, and Mauro Zigliotto, Member, IEEEAbstract-This paper presents a novel stand-alone solarpowered water pumping system, especially suited for usage in rural or remote areas. The system is primarily designed to reduce both cost and complexity, while simultaneously guaranteeing optimal utilisation of the photovoltaic generator. The use of standard hardware and control architectures ensures ease of installation, service and maintenance. The proposed solution consists of a water pump driven by a permanent magnet synchronous motor (PMSM), controlled by a conventional field oriented control scheme. The photovoltaic array is directly connected to the DC bus of the inverter, with no intermediate power conversion stages. A perturbation based extremum-seeking controller adjusts the motor speed reference to attain the maximum power point operation of the photovoltaic array. Both simulations and experimental results on a full-scale prototype support the effectiveness of the proposed system. Index Terms-PMSM drives, MPPT tracking, PV water pumping, extremum seeking control NOMENCLATURE Variables names convention (letter / subscript):• uppercase / uppercase: large-signal DC quantity.• lowercase / lowercase: small-signal quantity.• uppercase / lowercase: amplitude of a small-signal sinusoidal perturbation.• lowercase / uppercase: total quantities (i.e. small-signal perturbation superimposed on large-signal DC quantity) Symbols used: p P , u P PV array output power and voltage. p C , p I power stored in DC bus capacitor, power absorbed by the inverter. p R , p M Joule losses and delivered mechanical power. u D,Q , i D,Q PMSM voltages and currents in the dq frame.

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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 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 leastsquare\ud algorithm, returns an accurate on-line estimation of the electromagnetic torque. The effectiveness of the proposed method is proved by a worked out example, based on a 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