Model predictive control (MPC) for switched reluctance machine (SRM) drives is studied in this thesis. The objective is to highlight the benefits of implementing MPC to overcome the main drawbacks of SRMs and position them as an attractive alternative among electrical drives. A comprehensive literature review of MPC for SRM is presented, detailing its current trends as an application still at an early stage. The different features of MPC are highlighted and paired with the most challenging and promising control objectives of SRMs. A vision of future research trends and applications of MPC-driven SRMs is proposed, thus drawing a road-map of future projects, barriers to overcome and potential developments. Several important applications can take advantage of the improved features that SRM can get with MPC, especially from the possibility of defining a unified control technique with the flexibility to adapt to different system requirements. The most important cluster for SRM drives is the high- and ultrahigh-speed operative regions where conventional machines cannot work efficiently. SRMs with MPC can complement then the existing demand for electrical drives with high performance under challenging conditions. Three techniques based on the finite control set model predictive control (FCS-MPC) approach are developed out of the proposed road-map. The first one defines a virtual-flux current tracking technique that improves the existing ones in operating at different speeds and more than one quadrant operation. The method is validated for low- and high- power SRMs in simulations and diverse types of current waveform, making it easy to adapt to existing current shaping techniques. It is also validated experimentally for different operating conditions and robustness against parameter variation. The second technique proposed a predictive torque control that bases its model on static-maps, thus avoiding complex analytical expressions. It improves its estimation through a Kalman filter. The third technique uses a virtual-flux predictive torque control, similar to the first technique for current tracking. The techniques are validated at a wide speed range, thus evidencing superiority in performance without modification on the control structure. / Thesis / Doctor of Philosophy (PhD)

The increasing penetration of wind power into the power system dominated by variable-speed wind turbines among the installed wind turbines will require further development of control methods. Power electronic converters are widely used to improve power quality in conjunction with the integration of variable speed wind turbines into the grid. In this thesis, a detailed model of the Predictive Current Control (PCC) method will be descripts for the purpose of control of the grid-connected converter. The injected active and reactive power to the grid will be controlled to track their reference value. The PCC model predicts the future grid current by using a discrete-time model of the system for all possible voltage vectors generated by the inverter. The voltage vector that minimizes the current error at the next sampling time will be selected and the corresponding switching state will be the optimal one. The PCC is implemented in Matlab/Simulink and simulation results are presented.

The Permanent Magnet Synchronous Motor (PMSM, also known as the servo motor) is a crucial component within robotic servo systems. To optimally respond to the torque demands sent from the high-level motion controller, the PMSM current controller must track the reference with speed and precision. Nevertheless, the operation of servo motors could be compromised due to the nonlinearity of flux linkage and inaccuracies in parameters induced by unpredictable fluctuations in temperature. This Master’s thesis proposes a novel Robust Nonlinear Deadbeat Predictive Current Control (RN-DPCC) scheme to counter these challenges effectively. The nonlinear mappings between flux linkage and current on the dq-axis are established using polynomial fitting based on experimental data. Furthermore, the Nonlinear Deadbeat Predictive Current Control (N-DPCC) is derived using nonlinear feedforward. Meanwhile, Delayed Integral Action (DIA) is introduced as a robustness-enhancing measure for N-DPCC, thus evolving it into the Robust N-DPCC (RN-DPCC). Compared to conventional Integral Action (IA), DIA effectively curtails overshoot triggered by integral error and accelerates the current transient without incorporating additional tunable parameters. Numerical simulations that leverage the mathematical modeling of the converter and nonlinear PMSM are implemented using fundamental blocks in Simulink, which replicates the actual experimental setup employed within the Motor Control Lab at ABB Corporate Research. The effectiveness of employing nonlinear feedforward compensation is confirmed through a comparative analysis of the simulation results from N-DPCC and conventional Deadbeat Predictive Current Control (DPCC). The enhancements in transient response brought about by DIA are demonstrated through a comparison of RNDPCC and N-DPCC with IA. The robustness of RN-DPCC is demonstrated by comparing it with N-DPCC under conditions where parameter inaccuracies are present. / Den permanenta magnet-synkronmotorn (PMSM, även känd som servomotorn) är en avgörande komponent inom robotiserade servosystem. För att optimalt kunna reagera på momentkraven som skickas från högnivårörelsekontrollern måste PMSM-strömregulatorn följa referensen med hastighet och precision. Trots detta kan driften av servomotorer påverkas av ickelinjäriteter i flödeslänkningen och felaktigheter i parametrar som orsakas av oförutsägbara temperaturfluktuationer. Denna magisteravhandling föreslår en ny robust icke-linjär deadbeat-prediktiv strömreglering (RN-DPCC) för att effektivt hantera dessa utmaningar. De icke-linjära avbildningarna mellan flödeslänkning och ström på dq-axeln etableras med hjälp av polynomisk anpassning baserat på experimentella data. Dessutom härleds den ickelinjära deadbeat-prediktiva strömregleringen (N-DPCC) med hjälp av Ickelinjär feedforward. Samtidigt introduceras fördröjd integralåtgärd (DIA) som en robusthetsförbättrande åtgärd för N-DPCC, vilket förvandlar den till Robust N-DPCC (RN-DPCC). Jämfört med konventionell integralåtgärd (IA) minskar DIA effektivt överhäng som utlöses av integralfel och accelererar strömövergången utan att införa ytterligare justerbara parametrar. Numeriska simuleringar som utnyttjar den matematiska modelleringen av omvandlaren och den icke-linjära PMSM implementeras med hjälp av grundläggande block i Simulink, vilket återskapar den faktiska experimentella uppställningen som används i Motor Control Lab vid ABB Corporate Research. Effektiviteten i att använda icke-linjär framåtmatningskompensation bekräftas genom en jämförande analys av simulationsresultaten från N-DPCC och konventionell deadbeat-prediktiv strömreglering (DPCC). Förbättringarna i transientrespons som DIA medför demonstreras genom en jämförelse av RN-DPCC och NDPCC med IA. Robustheten hos RN-DPCC demonstreras genom att jämföra den med N-DPCC under förhållanden där parameterfel förekommer.