Compared to the widespread modulation-based control strategies such as field oriented control (FOC), MPC eliminates the use of internal current control loops and modulation block, hence featuring very quick dynamic response. Furthermore, MPC well respects the discrete nature of power converters by evaluating the influence of each voltage vector on the concerned control variables, which are usually combined into a cost function, hence featuring simple concept. In spite of the merits of quick response and conceptual simplicity, it seems that MPC may be inferior to FOC in terms of steady-state performance, especially in the low speed range, which is usually a drawback of MPC-branched methods. This study proposes an improved two-vectors-based MPC (MPC2) and compares it to the well-established FOC. A detailed experimental study of both methods is presented, including steady-state performance, dynamic response, low speed operation and control complexity. Very promising results are obtained in MPC2 by providing better steady-state performance and much quicker response than FOC under the condition of similar average switching frequency. The results prove that MPC2 is a powerful alternative to FOC and may find wide applications in the near future.

This study presents the design and implementation steps of a digital predictive controller to regulate a low-inductance, three-phase, three-wire permanent magnet brushless DC motor currents. These types of motors are usually driven by multi-stage converters, switched at high frequencies, or use additional inductances to limit the current ripple. The motor's trapezoidal back electromotive force and rectangular currents waveforms make the design and the tuning process of linear controllers difficult. This task complexity increases when a wide speed range is considered. Digital predictive controllers are easily implemented in digital signal processors (DSPs), being successfully used to regulate currents of different types of power electronic converters. A unipolar pulse width modulation predictive controller is proposed here to regulate the rectangular currents of a brushless DC motor, without the need for any additional filter or converter. Experimental and simulation results using a 5 kW/48 V three-phase brushless DC (BLDC) motor are presented to demonstrate the feasibility of this proposal. It will be presented a methodology to compensate the conditioning and sampling circuits delays as well as the inverter's semiconductors voltage drop. The control algorithm was implemented in a TMS320F28335 DSP.

The design and the experimental validation of a continuous-time model predictive control (CTMPC) for a permanent magnet synchronous motor (PMSM) drive with disturbance decoupling is discussed. The CTMPC approach uses Taylor series expansion to derive a closed-form solution to the problem of model predictive control even though the system behaviour is described by a non-linear model. This type of controller requires an exact knowledge of the system model to guarantee an accurate prediction of the system behaviour, while the PMSM is usually subjected to model uncertainties and external disturbances such as the load torque. Moreover, in the proposed approach, the predicted speed tracking error is directly used to determine the required voltage command without the need for a cascaded control scheme. As a result, the load torque is seen as unmatched disturbance which makes exact disturbance decoupling more challenging. To overcome such a problem, a non-linear disturbance observer is designed and combined with the CTMPC method to enhance the prediction accuracy under parameter variation and unknown load torque. The feasibility of the proposed approach is experimentally investigated, and good transient and steady-state performances are obtained.

This work focuses on two prime objectives. The first is to detect chaos in the fractional-order model of a permanent magnet synchronous generator (PMSG) and the second is to suppress chaos using a novel predictive control scheme in the fractional order sense. The main contributions of the work, therefore, lie in discovering the minimum commensurate order for which chaos exists in the fractional-order PMSG (FOPMSG), studying its dynamical behaviour ranging from Hopf bifurcation to stability analysis and the proposal of a single-state predictive controller to stabilise the chaotic FOPMSG in both commensurate as well as incommensurate orders. The proposal of predictive controller for controlling chaos in a fractional-order variable-speed drive is a novel attempt. Lower control effort, effectiveness, simplicity in design and application, convenience in inclusion of non-linearity, etc. highlight the promising potential of the proposed predictive control scheme. Circuit implementation results which are in good qualitative agreement with that of simulated results, confirm that the work attains the objectives successfully.

In this study, an improved model-predictive-flux-control (MPFC) is developed and implemented based on a three-phase flux-reversal permanent magnet (FRPM) machine drives fed by three-phase four-switch voltage-source-inverter (TPFS-VSI) with fewer switches and lower costs. The voltage variations and offsets across the two DC-link capacitors in this kind of inverter topology are suppressed by deriving an analytical expression of the DC-link capacitor voltages to reveal the inherent relationship between capacitor voltages and phase currents. In addition, the calculation process of the stator flux-linkage vector reference is simplified by adopting the dq-axes frame, and consequently, a precise predictive model is constructed for the stator flux-linkage vector control. Then, both the steady and dynamic performances of the TPFS-VSI-fed FRPM machine under the developed MPFC are evaluated by simulations and experiments. Meanwhile, the inverter and machine losses of the FRPM machine driven system of TPFS- and TPSS-VSI are measured for efficiency evaluation. The simulated and experimental results indicate that the developed MPFC not only offers the convergence of the DC-link voltages fed by the TPFS-VSI, but also improves the performance of the TPFS-VSI-fed FRPM machine drive in the low to medium speed applications.

A novel model-free predictive current control (MFPCC) approach for three-phase AC/DC converters is proposed, which only measures the input currents and calculates their differences under different switching states. Existing model-based predictive current control (MBPCC) methods are based on converter models and thus require the knowledge of input current, input and terminal voltages, resistance, and inductance. Compared to the MBPCC, the main advantages of the proposed MFPCC include eliminations of the converter model, multiplication operations, and tuning of system parameters. Simulation results show that the proposed MFPCC controls the input current slightly better than does the MBPCC. Experimental results are provided to validate the effectiveness of the proposed method, which are obtained from a three-phase AC/DC converter under the specifications of 380 V output voltage, 220 V/AC input voltage, and 1000 W output power. The measured harmonics orders 2–19 are shown to meet the IEC61000-3-2 Class A standard under 1000 W of output power. To the authors’ best knowledge, it is the first MFPCC for such applications.

This study presents a finite-control-set model predictive current control (FCS-MPCC) with phase shift compensation for a cost-effective voltage source inverter. Firstly, the FCS-MPCC algorithm uses the discrete-time model of the inverter to predict the coming value of the load current. In addition, a cost function is defined to evaluate all possible voltage vectors from the inverter, where the cost function is selected. Moreover, a pulse computation module is developed to obtain the final switching signals for insulated-gate bipolar transistors. In addition, the problem of current distortion caused by phase shift is solved by employing a time interval compensation factor. Furthermore, a comparison between the hysteresis control, pulse-width modulation control, and proposed predictive control is presented in terms of the steady-state and dynamic performances. Both simulation and experiment are conducted, which confirm the validity of the proposed control scheme of FCS-MPCC.

Finite control set predictive torque control (FCS-PTC) becomes popular for induction motor drives due to its simple structure and flexibility of including additional control parameters into the control law. However, primary concern of this control technique is the selection of suitable weighting factors in the cost-function. Usually, empirical method is used to select the weighting factors, which is time-consuming and heuristic process. In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced in the cost-function optimization to simplify the difficulties involved in the weighting factor selection. This method selects an optimal control action, which is closer to positive ideal control action and far away from negative ideal control action. This ensures the selection of optimal control action in each sampling period based on the priorities given to control parameters in the cost-function. Further, to reduce the computational burden of proposed technique, a predefined set of switching states are used for the cost-function optimization based on previous optimal control action. Both simulation and experimental studies are carried out for a two-level voltage source inverter fed induction motor drive. These results are compared with conventional FCS-PTC technique to highlight the merits of proposed technique.

The three-phase four-leg back-to-back converter-fed induction motor drive with only eight switches has the capability of variable-frequency speed control and bidirectional power flow. It can provide the benefit of higher reliability and less cost in comparison with the full-bridge back-to-back converter. On the other hand, the four-leg back-to-back converter can be utilised in fault-tolerant control to solve open-circuit fault occurring at both rectifier and inverter leg in a full-bridge back-to-back converter. However, the deviation of the two capacitor voltages which will lead to variation of voltage vectors in both amplitude and phase angle hinders its applications. This study proposes a control scheme based on finite-control-set model predictive control to remedy this disadvantage. With the proposed scheme, capacitor voltage deviation is suppressed. Bidirectional power flows and balanced input and output currents are achieved. The effectiveness of the proposed scheme is verified by the experimental results presented.

This study presents an enhanced predictive control strategy to reduce the calculation effort for direct matrix converters. The main idea is to preselect the switching states to decrease the calculation effort during each sample period. The proposed preselection algorithm enables a predefined cost function to consider only the preselected switching states to perform the expected control. On the basis of the preselection of switching states at each sample period, the proposed method can effectively reduce the calculation effort as well as show a good performance. The proposed predictive control scheme uses only preselected switching states to generate the desired source/load current waveforms and control the input power factor. The feasibility of the proposed method is experimentally verified and results are presented in the study.

Iterative and complex prediction loop is a challenge for the implementation of finite-state predictive torque control (FS-PTC) of motor drive. The complexity is due to the complex torque calculations, number of available voltage vectors (which are called as prediction vectors), and weighting factor tuning for torque and flux errors in the cost function. One way to reduce the complexity is an equivalent reference stator flux vector calculation (RSFVC) from torque and flux references, which also solves the problem of weighting factor tuning. Along with a new stator flux based RSFVC technique, a reduced number of prediction vectors are proposed in this study to reduce the number of iteration of the prediction loop. The position of the stator flux and sign of the stator flux-error are considered to lessen the number of prediction vectors. Hence, the implementation challenges of FS-PTC algorithm are overcome. The performance of the proposed technique is investigated for two types of RSFVCs: one is based on the stator flux, and another one is based on the rotor flux. Experimental results verify that the proposed low complexity FS-PTC strategies retain the advantages of a conventional FS-PTC.

Finite control set model predictive control (MPC) method needs calculations corresponding to the total number of voltage vectors made by power converters. This operating principle of the MPC leads to an increase in the computational load and number of calculations, which dramatically increase proportional to the increase in the level of cascaded H-bridge (CHB) inverters. However, it is difficult to consider all voltage vectors to find the optimal vector for high-level CHB inverters because of the short sampling time. This study proposes a new MPC algorithm to reduce the number of calculations for multi-level CHB inverters, in which only three voltage vectors are considered as optimal vectors regardless of the level of CHB inverters. Consequently, compared with the conventional MPC method for CHB inverters, the proposed MPC method requires fewer calculations and reduces the computational load without affecting the system performance and dynamic response.

Conventional model predictive direct torque control (MP-DTC) of permanent magnet synchronous generator (PMSG) suffers from weighing factor tuning work and relatively large calculation amount. This study proposes a simplified MP-DTC method without weighting factors for PMSG-based wind power system. First, the torque and stator flux magnitude are predicted on the stationary reference frame instead of on the synchronous rotating reference frame, hence reducing the calculation amount. Second, a new cost function based on the torque and the reactive torque is developed in this study. As the torque and the reactive torque have the same order of magnitude, the weighting factor which is needed in the conventional MP-DTC system is eliminated. Meanwhile, the stator current and stator flux magnitude can be controlled indirectly by controlling the torque and reactive torque simultaneously, which ensures the stability of the system. Besides, the robustness of the proposed strategy to unknown PMSG parameter variations is improved to a certain extent. The experimental results validate the effectiveness of the proposed method.

The multi-step direct model predictive control (M-DMPC) could reduce the switching frequency and improve the efficiency of the motor drive system while ensuring good steady and dynamic performance. However, the conventional M-DMPC, which uses the exhaustive optimisation method is with exponential time complexity, so it is difficult to realise in a short sample period. This study proposes a multi-step direct predictive torque control (M-DPTC) algorithm for two-level voltage source inverter fed surface-mounted permanent magnet synchronous motor (SPMSM) drive system. In the proposed algorithm, first, in order to reduce computation time in multi-step predictive process, a simplified multi-step predictive model is established. Compared with the complex operations, like square root and trigonometric function used in the conventional predictive model, only look-up table and addition operation are utilised in the proposed model for multistep prediction. Second, compared with the exhaustive searching method, the proposed optimisation method avoids the exploration of all possible switching sequences and has logarithmic time complexity. Combining the above two measures, the proposed M-DPTC can be achieved in a relatively shorter sample period. Simulation and experimental results for a 6-kW SPMSM are presented to validate the proposed algorithm.

This study is for model predictive pulse-width modulation (PWM) for power electronics converters as AC motor drives. Compared with conventional PWM, model predictive PWM (MPP) employs the prediction model of PWM effect, and utilises the pulse arrangement freedoms to improve the performance of switching losses, current ripple and electromagnetic interference for the converter system. This study contains five parts including introduction, current ripple prediction method, variable switching frequency PWM, MPP for multi-level inverters and conclusions. The methods introduced in this study have been and can be applied in traction and industry motor drives.

This research tends to provide a novel exploration state for finite control set-model predictive control. The aim of this study is to reduce the line current ripple at a given average switching frequency. The proposed method with hybrid exploration state divides the space vector plane into two regions. Each conventional exploration state is applied to a region, which would result in a lower root mean square current ripple. The above-mentioned procedure is based on the calculation of the stator flux ripple in the stationary reference frame during the sampling time. Consequently, a simple expression based on the magnitude and the phase angle of the reference voltage vector will be obtained, which can be employed as a measure for the current ripple. The superior performance of the proposed method over the conventional exploration state methods is verified by the theoretical results and the experimental tests.

This study proposes a finite control-set model predictive current control (FCS-MPCC) scheme based on virtual voltage vectors for five-phase permanent magnet synchronous machines, which aims to reduce the low-order harmonic components of stator currents. In addition, comparing with traditional FCS-MPCC algorithms, the proposed algorithm retains fast control dynamic response and brings several other benefits: such as smaller burden of calculation, approximate constant switching frequency in entire speed operation range and lower current harmonics, especially for three-order harmonics. Furthermore, the one-step delay compensate solution of the proposed FCS-MPCC scheme in digital-controller implementation is presented. Finally, a series of computer simulation and hardware-in-loop (HIL) experimental tests are adopted to compare the performances of the proposed FCS-MPCC and two traditional FCS-MPCC schemes. Simulation and HIL experimental results have verified the validity and effectiveness of the proposed FCS-MPCC scheme using virtual voltage vectors.

To reduce the torque ripple of permanent magnet synchronous motor with non-sinusoidal flux distribution, a new method is presented in this study. This method is based on model predictive control (MPC). MPC is a model-based control and requires an accurate model of the motor. A sliding mode observer, accompanied with a recursive least square estimator, is utilised to determine the magnitudes of the harmonics of the back electromotive force (EMF) waveforms. The appropriate current harmonics, considering the back EMF harmonics, are injected to shape the motor current. The interaction of non-sinusoidal back EMF and the shaped current leads to torque ripple reduction. The experimental results verify the effectiveness of the proposed method.

The indirect model predictive control (I-MPC) is one of the reduced computational predictive strategies, used to control the modular multilevel converter (MMC). This approach operates at higher switching frequency, which is not desirable for high-power applications. This study proposes an integrated solution for MMC by combining predictive control with the classical energy balancing approach. To implement the predictive algorithm, a detailed three-phase MMC model is presented. The three-phase model includes the zero sequence voltage to reduce the switching frequency of submodules. In addition, the output power quality is enhanced, while operating at reduced switching frequency. The performance of integrated approach is experimentally validated on a laboratory prototype under balanced and unbalanced conditions. In addition, the performance of integrated approach is compared with the existing methodology in terms of output current ripple, switching frequency, computational complexity, and total harmonic distortion.

Model predictive control (MPC) is a model-based control strategy that introduces many advantages but also some drawbacks. The biggest challenge that faces MPC controller is its sensitivity towards parametric variation. This study aims to robustify MPCs by adding a least mean square (LMS) identification algorithm within their structure. The feasibility of this method is tested on a simple example which is the MPC current control of a permanent magnet synchronous motor driven by a two-level power converter. Many tests are firstly performed without including inside the MPC algorithms the LMS online tuning procedure. These tests are later compared with the ones obtained with the same control technique but where the identification algorithm is turned on. Results clearly show all the benefits of using this approach on the controller performances.

Finite-control-set model predictive control (FCS-MPC) has many advantages in electric drive control systems but needs the accurate knowledge of the system parameters. The performance of the FCS-MPC will be deteriorated under parameter mismatches. This study proposes an adaptive FCS-MPC current control method for interior permanent magnet synchronous machine (IPMSM) drives subject to the inductance variations. The inductances are identified online by an adaptive observer with a recursive algorithm, which is inherently incorporated into the FCS-MPC control process to reduce the additional computational cost. Compensation methods are also proposed to improve the identification accuracy. The simulation and experimental results validate that, the IPMSM current control performance, speed-extension capability and drive efficiency are all improved by the proposed method.

Sensorless predictive current control (PCC) for an induction machine is proposed in this study. In the controller, the errors between the current references and the predicted values are evaluated; therefore, it guarantees good current behaviours which are essential to the low-voltage drives. By designing proper adaptive scheme and gain matrix, a stable sensorless PCC system is achieved. Furthermore, the sensitives of parameters such as stator resistance and magnetic inductance are discussed with both experimental results and theoretic analysis. The sensorless system is carried out experimentally in a very wide-speed range both with and without loads.

In this study, a speed-sensorless finite control set-model predictive current control method is proposed based on an adaptive full order observer. The control system features simplicity and low cost because of no requirement for speed measurement, modulator and tuning of weighting factors. In most sensorless based schemes, the motor is assumed to be started from standstill. There is limited research considering starting a free running motor with unknown rotational direction and speed. To start a free-running induction motor (IM), the feedback gain matrix is designed to guarantee the convergence of estimated speed to actual speed even with incorrect initial value. To improve the efficiency, amplitude of flux is adjusted along with load condition. The presented results show that the proposed method is able to smoothly start an IM with unknown initial speed and work well over a wide speed range. The effectiveness of the proposed method is verified by both simulation and experimental tests on a two-level inverter fed IM drive platform.

This study proposes implementation of a predictive controller for a sensorless synchronous reluctance drive system. First, high-frequency voltage is injected in the α-axis. Then by measuring the α–β axis high-frequency currents, the rotor position of the SynRM can be estimated. After that, a predictive speed controller is implemented to improve transient responses, load disturbance responses, and tracking responses. To demonstrate its viability, a proposed 560 W sensorless drive system is implemented by using a TMS-320F-28335 DSP made by Texas Instruments and some circuits. Experimental results clearly indicate that the proposed high-frequency α-axis voltage injection method with a predictive speed-loop controller offers superior responses including faster transient responses, better load disturbance responses, and better tracking responses than a high-frequency d–q axis voltage injection method with a PI speed-loop controller.

The growing energy market and increasing energy costs require new solutions in pumping technology aiming to rise the energy efficiency. In the study, a novel centrifugal pump predictive control organisation is proposed. Unlike the traditional speed-oriented pump regulation, an efficiency-oriented approach is realised where the specific power loss in the motor drives are taken into account alongside the general pump and pipeline losses. The offered management strategy and algorithm are based on the prediction of optimal operating points using combined tabulated and analytical performance characteristics of a pumping station. Applying the efficiency distribution diagram, the control map has been designed intended for running the optimal number of pumps at the appropriate speeds. As a result, the system performance within one of the best efficiency regions is maintained now at all the requested head and pressure inputs and pipeline condition instabilities.

Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation and experimental results demonstrate the flawless performance of the suggested controller and the fast and accurate tracking of the EV speed to its set-point.

Switched-reluctance technology is appealing to the automotive industry due to its low cost. Conventional research focuses on the two main issues, namely torque ripple and acoustic noise. However, control schemes usually only address one of these two issues at a time. This study unifies the approaches of direct instantaneous torque control and direct instantaneous force control to simultaneously eliminate two central drawbacks of switched reluctance machines (SRMs). A search algorithm was used to predict appropriate reference values for each phase to keep torque and overall radial force smooth at any time. Simulation and measurement results confirmed that the control objectives were met so that torque ripple was reduced and the acoustic behaviour of the investigated automotive traction drive was simultaneously improved. A comparison with conventional control methods investigating vibrations and efficiency is provided. The proposed control approach thus provides future engineers with a powerful tool for adapting SRMs to the requirements of the automotive sector.

This study presents an online multiparameter estimation scheme for interior permanent magnet motor drives that exploits the switching ripple of finite control set (FCS) model predictive control (MPC). The combinations consist of two, three, and four parameters are analysed for observability at different operating states. Most of the combinations are rank deficient without persistent excitation (PE) of the system, e.g. by signal injection. This study shows that high frequency current ripples by MPC with FCS are sufficient to create PE in the system. This study also analyses parameter coupling in estimation that results in wrong convergence and propose a decoupling technique. The observability conditions for all the combinations are experimentally validated. Finally, a full parameter estimation along with the decoupling technique is tested at different operating conditions.