Finite Set Model Predictive Control Research Articles

Disturbance Observer Based Predictive Current Control of Six-Phase Permanent Magnet Synchronous Machines for the Mitigation of Steady-State Errors and Current Harmonics

Multiphase machines, associated with state-of-the-art control strategies like finite control set model predictive control (FCS-MPC) are suitable for applications where reliability and disturbance-free operation are key factors. Although FCS-MPC provides good transient performance and straightforward design, the performance of the drive system is affected by model inaccuracies due to parameter mismatch and unmodeled dynamics, such as deadtime effects in the power converters and back-electromotive force harmonics. In this context, this article proposes a novel and robust disturbance observer based predictive current control strategy for six-phase permanent magnet synchronous machine drives that minimizes the steady-state errors due to parameter mismatch errors, while simultaneously reducing the amplitude of the fifth- and seventh-order current harmonics due to unmodeled dynamics, which are non-negligible in six-phase machines. Simulation and experimental results demonstrate the very good performance of the proposed strategy even when considering significant errors in the system parameters and different values of the deadtime in the power converters.

Multistage Predictive Current Control Based on Virtual Vectors for the Reduction of Current Harmonics in Six-Phase PMSMs

The most recent finite control set model predictive control (FCS-MPC) strategies for six-phase electric drives avoid the excitation of the secondary subspace by using virtual vectors to minimize the current ripple. However, these strategies can only control the fundamental current components, which adjust the machine flux and torque and leave the secondary current components unregulated, which should be zero during regular operation in order to reduce the losses in the machine. Due to this limitation and also due to the low impedance of the secondary subspace in machines with distributed windings, the stator currents might contain non-negligible low-frequency harmonics caused by small asymmetries in the machine, non-linear dynamics of the semiconductors in the power converters and back-EMF harmonics. This article proposes a novel multistage predictive current control strategy based on virtual vectors (MVV-PCC) for six-phase drives based on permanent magnet synchronous machines (PMSMs), which contains a fundamental and a secondary control stages that are responsible for tracking the reference currents in both subspaces separately. The obtained experimental results validate the effectiveness of the proposed strategy in the minimization of the current harmonics and the reduction of the torque ripple for different operating conditions of the six-phase drive.

Stable Shortest Horizon FCS-MPC Output Voltage Control in Non-Minimum Phase Boost-Type Converters Based on Input-State Linearization

Finite Control Set Model Predictive Control (FCS-MPC) is an alternative to conventional linear controllers in power converters due to its attractive properties such as its conceptual simplicity, flexibility, direct consideration of non linearities as well as constraints, and fast control response. This last feature imposes computational constraints and short prediction horizons are therefore needed. In fact, in many applications an horizon one FCS-MPC controller is used. However, using FCS-MPC with a short prediction horizon in topologies that exhibit non-minimum phase behavior such as active-front-end rectifiers, dc-dc boost converters, and Z-source converters, among others, may lead to control issues. This work studies the origin of these control issues using nonlinear control theory considering a dc-dc boost converter as a case study. It is shown that the difficulty to directly control the converter output voltage using short horizon FCS-MPC is the resulting unstable internal dynamic, as the short horizon FCS-MPC controller acts like an input-output linearizing controller. To solve the problem of the unstable internal dynamic, a short horizon FCS-MPC controller based on input-state linearization is proposed allowing correct operation of the system. Experimental results show the feasibility of the proposal.

A Sphere Decoding Algorithm for Multistep Sequential Model-Predictive Control

This article investigates the combination of two model-predictive control concepts, i.e., sequential model-predictive control and long-horizon model-predictive control, for power electronics. To achieve sequential model-predictive control, the optimization problem is split into two subproblems. The first one summarizes all control goals, which linearly depend on the system inputs. Sequential model-predictive control generally requires to obtain more than one solution for the first subproblem. Due to the mixed-integer nature of finite control set model-predictive control power electronics, a special sphere decoder is, therefore, proposed in this article. The second subproblem consists of all those control goals that depend nonlinearly on the system inputs and is solved by an exhaustive search. The effectiveness of the proposed method is validated via numerical simulations at different scenarios on a three-level neutral-point-clamped permanent magnet synchronous generator wind turbine system and compared to other long-horizon model-predictive control methods.

Finite Control Set Model Predictive Control for AC–DC Matrix Converter With Virtual Space Vectors

AC–DC matrix converter is a kind of bidirectional converter features buck-type rectifying and boost-type inverting. Considering the coupling of ac current and dc current, and the requirement on safe commutation, the finite control set model predictive control (FCS-MPC), featuring multiple-objective capability and direct generation of gating signals without pulse width modulation (PWM) scheme, is very suitable for this topology. Also, fast transient can be obtained with the FCS-MPC. However, in conventional FCS-MPC, only one space vector in each sampling period is applied. The performance will be degraded due to the low switching frequency under limited sampling frequency in practice. In this article, an FCS-MPC with virtual space vectors is proposed for a bidirectional ac–dc matrix converter. This article uses several virtual space vectors, each formed by two real space vectors, to improve the control performance without increasing sampling frequency. To further reduce the computation burden, an efficient method to preselect space vectors is proposed. Furthermore, a thorough analysis of the effect of parameter mismatch in the proposed FCS-MPC is conducted to ensure robust performance. Both simulation and experimental results are presented to verify the effectiveness of the proposed control strategy.

Virtual-Vector-Based FCS Model Predictive Current Control with Duty Cycle Optimization for Dual Three-Phase Motors

This paper presents an improved finite control set model predictive current control (FCS-MPCC) based on virtual vectors with duty cycle control for dual three-phase permanent magnet synchronous motor (DTP PMSM) drives. Virtual vectors synthesized by two basic voltage vectors of the inverter are introduced to suppress the harmonic current by the vector space decomposition (VSD) model. And the virtual vectors with the optimal duty cycle can be obtained by duty cycle control using zero vectors to generate a fixed switching frequency. Moreover, a simple scheme of centrosymmetric switching patterns is presented for the realtime implementation. In this way, the performance of current tracking is improved while the computational burden is reduced. Finally, results of simulation analysis and DSP test for the three different strategies are given to verify the validity of the proposed strategy.

Hybrid Model Predictive Control for Modified Modular Multilevel Switch-Mode Power Amplifier

This article proposes a hybrid model predictive control (HMPC) for a modified modular multilevel switch-mode power amplifier (M3-SMPA). The M3-SMPA consists of two parts connected in series including modular multilevel converter (MMC) and full-bridge converter (FBC). Different from available MPC methods that only apply either finite control set MPC (FMPC) or modulated MPC (MMPC), the proposed HMPC method synthesizes the merits of both FMPC and MMPC on different timescales. First, the optimal control option (CO) for MMC is calculated by FMPC with the multiple control objectives, including output voltage control, circulating current control, and submodules (SMs) capacitor voltages balance control. Then, based on the above-mentioned optimal CO, the optimal duty cycle for FBC is calculated by MMPC with a single objective of output voltage control. In this case, the FMPC achieves the multiobjective control of MMC while the MMPC eliminates the tracking error of output voltage using FBC with a fixed switching frequency. Furthermore, a circulating current injection method is presented to balance the SMs capacitor voltages and an improved adjacent search method is introduced to reduce the evaluated COs for MMC in each control period. The effect of dc-link voltage configuration for FBC on output steady-state performance is also analyzed. Finally, the effectiveness of the proposed HMPC method is verified by experimental results.

A Robust Torque and Flux Prediction Model by a Modified Disturbance Rejection Method for Finite-Set Model-Predictive Control of Induction Motor

The extended state observer (ESO) has been found as an efficient solution for the model sensitivity in the model-predictive control (MPC). Generally, the ESO is used for disturbance rejection when the voltage reference is the output of the control law. The observed disturbance is subtracted from the control law. Thus, using disturbance rejection for finite-set MPC is a challenge when it is utilized by a cost function consisting of the errors of the torque and the flux. In this research, a modified disturbance rejection method is used in a feedforward shape to improve the robustness of the finite-set model-predictive torque control. In this regard, the motor parameters have not appeared in the torque prediction algorithm. Only a rough approximation of the motor parameters is needed to design the ESO, which means that the proposed method has a very low dependence on the motor parameters. Besides, a thorough tuning guideline is proposed to tune the local parameters based on the convergence analysis of the ESO by using the self-stable region approach and the Lyapunov function. The performance of the proposed MPC scheme is evaluated through simulations and experimental tests. The results of the proposed method are compared with the classic MPC results.

Research on suspension control strategy based on finite control set model predictive control with state feedback control‐PID for maglev yaw system of wind turbine

The maglev yaw system (MYS) of wind turbine adopts the maglev-driving technique instead of the traditional gear-driving technique, so it has the advantages of short downtime and low cost of operation and maintenance. However, it is vulnerable to the nonlinear time-varying disturbance caused by the random change of wind speed and direction. This paper proposes a dual-loop suspension control strategy based on the finite control set model predictive control (FCS–MPC) with state feedback control (SFC) to improve the dynamic response and anti-disturbance ability of the MYS. First, the mathematical models of the MYS are built. Second, in order to realise the stable suspension control, the outer loop controller for suspension air gap tracking is designed by combining SFC with proportional integral differential, and the inner loop controller for maglev current tracking is designed by adopting FCS–MPC with delay compensation. Finally, the simulation and experimental results show that the proposed control strategy has better robustness and dynamic response property comparing with the existing control strategies, and the maglev system of MYS can realise smooth and reliable operation. The proposed suspension control strategy is substantiated to be effective and feasible.

Floating Weighting Factors ANN-MPC Based on Lyapunov Stability for Seven-Level Modified PUC Active Rectifier

Despite being cost-effective, seven-level Modified Packed U-Cell (MPUC7) active rectifier tends to be unstable due to unequal dc-links. Thus, a multiobjective controller is required to stabilize voltages and currents besides preserving efficiency and power quality. While conventional finite-set model predictive control (FSMPC) can deal with the multiobjective problem, it cannot assure the system stability, and its weighing factors tuning significantly becomes tiresome as the number of objectives increases. This article presents a low-frequency adaptive FSMPC (AMPC) stabilized based on Lyapunov stability theory to overcome the design problems of FSMPC. AMPC handles four control objectives and a decoupled stability objective. The control objectives assure the standard performance of MPUC7 in terms of switching losses, d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> /d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> , THD, and capacitors ripple. The stability objective guarantees the rectifier reliability under unstable conditions. The weighting factors in AMPC are floating to tackle the tuning challenges where a radial basis function neural network controller (RBFC) adjusts their variations. RBFC is trained by a novel self-training method including particle swarm optimization (PSO) algorithm and some mathematical analyses without using any training data. Experimental and simulation tests also evaluate AMPC in different conditions to confirm its reliability in fulfilling the desired objectives.

Observer-Predictor-Based Predictive Torque Control of Induction Machine for Robustness Improvement

In the finite control set model predictive control (FCS-MPC) of induction motors, the mismatched motor parameters will inevitably lead to model predictive errors, and then reduce the control performance. At present, the evaluation methods of the prediction errors caused by mismatched parameters and of improving robustness have become the research hotspots of FCS-MPC of induction motors. In this article, an observer-predictor-based predictive torque control of induction machine is proposed in order to improve the prediction accuracy. In the stator current prediction equation, this article abandons the traditional open-loop prediction equation and proposes a closed-loop stator current prediction equation. In the stator flux prediction equation, this article abandons the traditional stator flux prediction model based on voltage model, and proposes a discrete hybrid prediction model combining voltage model and current model, where the proportional-integral regulator is used to switch the voltage model to the current model. Second, the stability and parameter design principles of the closed-loop stator current prediction model and the closed-loop stator flux prediction model are analyzed. Finally, the experimental results show that the proposed method is superior to the traditional method in dynamic performance, steady-state performance, and parameter robustness.

Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.

Finite control set model predictive control integrated with disturbance observer for battery energy storage power conversion system

A typical battery energy storage system consists of a combination of battery packs and a grid-tied power conversion system. The control algorithm of the power conversion system plays an important role when interfacing the DC energy stored in battery packs with the conventional AC grid to generate an obedient bidirectional power flow. Finite control set model predictive control is believed to be one of the most effective choices for controlling power conversion systems. However, the performance of such a control strategy heavily depends on the accuracy of the predictive model. Parameter mismatch in the model leads to prediction error, which deteriorates the overall power quality performance of the power conversion system. Therefore, this paper studies a robust finite control set model predictive control method based on a discrete disturbance observer to eliminate the negative effects caused by model inaccuracy and uncertainty. The stability issue of the additional observer is discussed from the perspective of closed-loop poles. Parameter scan is performed to provide assistance in designing the feedback matrix. Finally, simulations and experimental results obtained from a downscaled prototype rated at 4.2 kVA are conducted as a validation of the presented control algorithm.

Model Predictive Controller Utilized as an Observer for Inter-Turn Short Circuit Detection in Induction Motors

In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.

Hybrid Model Predictive Control of DC–DC Boost Converters With Constant Power Load

This article presents a hybrid model predictive controller to ensure dc microgrid stability and enhance the performance of dc-dc boost converters interfaced with constant power loads (CPLs) in a hybrid system. Hybrid systems are dynamic systems with both continuous current mode and discontinuous current mode states. The main purpose in this article is to develop an advanced control technique for voltage regulation and stabilization of the converters in the presence of CPLs due to serious stability concerns, without considering the accurate modelling information of the system. In this regard, an automatic model, considering different modes of operation induced by semiconductor switches in dc-dc boost converters and highly non-linear nature of CPL is employed to design the proposed control approach. The non-linear CPL connected directly to a dc-dc boost converter is utilized to define an optimal tracking control problem by minimizing a finite-prediction horizon cost function, which is known as a finite control set MPC. The proposed controller, which is implemented in both continuous and discontinuous current modes, accounts for the regulation of output voltage within the predefined range. The effectiveness of the proposed hybrid model predictive control is verified using a comparative evaluation with discrete-time averaged model predictive control, continuous control set MPC, and the conventional PI control under experimental conditions. The results authenticate an improved dynamic performance, which can be applied to practical dc microgrids with CPLs.

Very Low Sampling Frequency Model Predictive Control for Power Converters in the Medium and High-Power Range Applications

Several control strategies have been proposed with the aim to get a desired behavior in the power converter variables. The most employed control techniques are linear control, nonlinear control based on linear and nonlinear feedback, and predictive control. The controllers associated with linear and nonlinear algorithms usually have a fixed switching frequency, featuring a defined spectrum given by the pulse width modulation (PWM) or space vector modulation (SVM) time period. On the other hand, finite set model predictive control (FS-MPC) is known to present a variable switching frequency that results too high for high power applications, increasing losses, reducing the switches lifetime and, therefore, limiting its application. This paper proposes a predictive control approach using a very low sampling frequency, allowing the use of predictive control in high power applications. The proposed method is straightforward to understand, is simple to implement, and can be computed with off-the-shelf digital systems. The main advantage of the proposed control algorithm comes from the combination of the model predictive control and the SVM technique, drawing the principal benefits of both methods. The provided experimental results are satisfactory, displaying the nature of space vector-based schemes but at the same time the fast response as expected in predictive control.

Finite control set model predictive control of quasi-z-source inverter photovoltaic grid-connected

The PI control of the traditional quasi-Z inverter(qZSI) has difficulty in parameter correction and the shortcomings of hysteresis, while the traditional model predictive control (MPC) has a slower response and less precision for current control. In this paper, the current is transformed to the d-q coordinate system, and a suitable output vector is selected according to the limited switch state, which can track the reference current and perform finite set model predictive control (FCS-MPC). Compared with the traditional predictive control method, the current distortion rate is reduced, so that the photovoltaic inverter has a better output result.

FCS-MPC Based Pre-Filtering Stage for Computational Efficiency in a Flying Capacitor Converter

Finite Control Set Model Predictive Control (FCS-MPC), thanks to its simple and powerful concept, has been applied widely to control the multilevel converters. FCS-MPC allows the control of several objectives and can incorporate restrictions, even nonlinear, into the mathematical model of the system under consideration. However, FCS-MPC needs to evaluate all the switching states of the multilevel converter to find the best switching state to be applied in the next sampling time. For higher-level multilevel converters, this iterative action requires computational capacity that is far beyond the digital controller’s capacity in the current market. This paper proposes a new predictive geometric pre-filtering strategy to reduce the iterations and computational burden without affecting the dynamic performance of FCS-MPC. This method consists of a novel pre-filtering stage that uses the predictive model of the system and geometrical properties to find the sector where the reference vector is located and evaluates few vectors that constitute the optimal sector. The proposed method is experimentally validated using a four-level three-cell flying capacitor converter with 512 voltage vectors, obtaining a 64% reduction in the computational burden, while achieving excellent electrical performance indices and maintains the high dynamic performance of the standard FCS-MPC.

Current Harmonic Mitigation Using a Multi-Vector Solution for MPC in Six-Phase Electric Drives

Multiphase machines offer some interesting features to develop more-competitive electric drives. However, the control complexity increases due to the higher number of freedom degrees. Specifically, the regulation of secondary currents becomes critical to avoid an unacceptable harmonic distortion of phase currents. In this regard, standard finite-control-set model predictive control (FCS-MPC) is characterized by a limited capability to provide a suitable current quality, since a single switching state is applied per control cycle. In order to reduce the current harmonic content and retain the well-known FCS-MPC advantages, the use of a multi-vector approach has been recently explored in the field of multiphase electric drives. Following this trend, this work develops a simplified FCS-MPC version with a new generation of virtual voltage vectors (VV) that are used as control actions. The switching states that form the proposed VV provide null average $x-y$ voltages and reduced instantaneous injection of these secondary components. The capability of the suggested VV-based FCS-MPC strategy to mitigate the $x$ – $y$ injection is experimentally tested and compared to field-oriented control (FOC) using carrier-based pulse width modulation (CB-PWM) and diverse FCS-MPC schemes.

Maximum Energy Capturing Approach for Heaving Wave Energy Converters Using an Estimator-Based Finite Control Set Model Predictive Control

The control problem in wave energy continues to remain an open question. This is mainly attributed to the difficulties associated with developing effective, yet economically viable, wave energy-harnessing control strategies, such as resource irregularity, the multidisciplinary nature of the system, and dynamic model uncertainties and ambiguities. Herein, a maximum energy-capturing approach for heaving wave energy converters (WECs) using an estimator-based finite control set model predictive control (FCS-MPC) is proposed. The proposed control strategy utilizes an elaborate nonlinear wave-to-wire model of a heaving WEC. The FCS-MPC is formulated such that a control command trajectory is not required; instead, it searches for the optimum control law—in the form of switching functions—that maximizes the WEC converted electrical energy while imposing soft constraints on the states of the power take-off (PTO) mechanism. Current transducers are deployed to measure the PTO three-phase currents and both mechanical and electrical variables required by the FCS-MPC strategy are estimated using an electrical-based extended Kalman filter (E-EKF). Simulations were performed to assess the effectiveness of the proposed control strategy. Results presented herein clearly show that the proposed referenceless FCS-MPC managed to produce 10%–23% more energy compared with benchmark resistive loading-based techniques with both fixed and variable wave frequency capabilities while utilizing 18%–45% less PTO resources.