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Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Featured Application: The proposed control strategy is specifically designed for applications with non-minimum phase behavior. It can be effectively applied to DC-DC boost converters, dual active bridge converters, and other topologies like buck-boost, Cuk, SEPIC, and ZETA converters, all of which commonly exhibit non-minimum phase characteristics. Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller's behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method's effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system's robustness and potential for real-time applications in power electronics. [ABSTRACT FROM AUTHOR]

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains Kd and Kq for direct and quadrature axes, respectively, with the FCS gain Kfcs obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of Kd = 0.01, Kq = 0.006, and Kfcs = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.

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This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains Kd and Kq for direct and quadrature axes, respectively, with the FCS gain Kfcs obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of Kd = 0.01, Kq = 0.006, and Kfcs = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models. [ABSTRACT FROM AUTHOR]

This paper presents a finite-control-set model predictive control (FCS-MPC) with fractional-order proportional-integral (FO-PI) controller to regulate the dc-link voltage of a single–phase cascaded H–bridge (CHB) 5-level inverter working as a shunt active power filter (APF). By using the FCS-MPC scheme in a single–phase CHB multilevel inverter as an APF, it is possible to generate a compensation current that tracks its reference with maximum precision, in addition, it presents a quick dynamic reaction to disturbances. When the compensation current is injected into the electrical system, current harmonic distortions are effectively reduced. To simplify the number of evaluations in the algorithm, the voltages on the dc-link of each H-bridge are equalized by a redundant selection of switching states of the multilevel inverter. The discrete-time model of the single-phase shunt APF, the technique to generate the compensation current reference signal, and the developed FCS-MPC algorithm are explained in detail. Furthermore, to keep the dc-link voltage steady, with minimal disturbance, it is proposed to use a discrete-time FO-PI controller. The effectiveness of the proposed control scheme is investigated in steady-state, as well as dynamic transients caused by sudden load changes. The developed control algorithm is verified through experimental tests conducted on a 5 kVA single-phase CHB 5-level inverter-based shunt APF.

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics.

Este trabalho apresenta uma estratégia de controle preditivo de torque para o motor de indução monofásico. A técnica de controle proposta emprega o modelo matemático dinâmico dos enrolamentos auxiliar e principal para a realização da predição do fluxo e do torque do motor. O estado do inversor é escolhido com emprego da função custo minimizada que processa o erro entre as referências de torque e fluxo com suas respectivas predições. Tem como vantagens frente à outras técnicas conhecidas a simplicidade de implementação, não necessidade de ajuste de ganhos como controladores PI e a não necessidade de um algoritmo de modulação por largura de pulsos. Visto que é uma técnica ainda pouco empregada. Os resultados obtidos em bancada experimental corroboram com a proposta deste trabalho.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER). The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power; so that both power components (P and Q) are well controlled to their reference values. In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag. The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

Model Predictive Control algorithms have been recently developed for controlling grid-tied converters. However, the inclusion of the converter switching constraints in the optimization problem and the high computational burden are some of the main challenges of these algorithms. In this way, this paper proposes a Fast Finite Control Set Model Predictive Control algorithm with a low computational burden for a three-phase Neutral Point Clamped inverter considering its switching constraints. Initially, the vector with the unconstrained solution in the line-to-line voltage coordinates is obtained to minimize the current tracking error. Then, it is limited to ellipses as an intermediate step to ensure that the selected voltage vector is feasible and to restrict the switching transitions. The constrained vector is rounded to the nearest inverter line-to-line voltage vector to be implemented in the next sampling period. The NPC redundant phase-voltage vectors are generated online to avoid the potentially destructive switching transitions. The neutral point is balanced by minimizing a cost function, considering the obtained redundant phase voltage vectors, and is evaluated at most twice in each sampling period. As both control objectives are treated in a cascaded sequence, the proposed Fast FCS-MPC avoids the design of weighting factors and has the advantages of low computational burden, fast transient response, and good steady-state performance. Finally, Hardware-in-the-Loop results are presented to compare the proposed Fast FCS-MPC to other strategies presented in the literature, and the effectiveness of the proposed algorithm is also demonstrated by means of an experimental prototype. • System model in the converter output line-to-line voltage space with integer vectors. • Inverter constraints included in the formulation of the optimization problem. • Fast way to obtain the switching voltage vector by rounding the constrained solution. • Comparison results indicating equivalent performance with optimal algorithms. • Lowest computational burden when compared to similar algorithms. [ABSTRACT FROM AUTHOR]

Shunt active filters (SAFs) can be used for harmonic mitigation, reactive power compensation and power factor control in electrical distribution networks. Model predictive controller (MPC) can be used to give optimum performance for SAF's due to its inherent cost function optimization. The SAF's reference current extraction technique in finite control set-MPC (FCS-MPC) plays a vital role in the effectiveness of the SAF's operation. The advantage of FCS-MPC is that it closely tracks the reference and injects the required compensating currents by optimizing the SAF's switching vector and it does not require an external modulator for pulse generation. FCS-MPC can be implemented as either an optimal switching vector MPC (OSV-MPC) or an optimal switching sequence MPC (OSS-MPC) technique. The OSV-MPC technique is simple and efficient for SAF applications. In this paper, a novel reference current extraction technique, the inverse matrix average pq-synchronous reference frame (SRF) (IMApq-SRF) technique, is proposed along with the OSV-MPC technique. Practically a two-step delay compensation is essential in the implementation of the technique for the control of an SAF. Hence in this paper a two-step horizon OSV-MPC technique is proposed along with an IMApq-SRF reference current extraction technique to improve the power quality in distribution networks. The presented results show that the proposed methodology gives optimum SAF performance under a large range of supply and load conditions. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) is a control strategy with fast response and a simple and flexible structure. However, when the control plant is complicated such as a multiphase electric machine, the application of FCS-MPC faces clear challenges. This article for the first time investigates the FCS-MPC for a nine-phase open-end winding (OW) permanent magnet synchronous machine, which is powered by nine H-bridge inverters with a common dc bus. First, in order to solve the challenge of substantial iterations in the conventional FCS-MPC, the number of control sets is simplified by reconfiguring the high level in switching states. Then, to eliminate the zero-sequence current caused by the common dc bus, the zero common-mode voltage (CMV) vector is selected. Subsequently, duty-ratio optimization is used to further reduce the available vectors. By the abovementioned measures, the number of iterations is reduced from 19 171 to 18. In order to suppress the harmonic current, the virtual voltage vectors (VVs) are designed. Each VV is synthesized by two zero CMV vectors, which can eliminate all the third and fifth harmonics in the output voltage. In addition, to achieve symmetrical pulsewidth modulation pulse sequences, a general pulse generation method for OW drive systems is proposed. Finally, the control performance of different control sets and harmonic weighting factors are evaluated and compared, and the experimental results have verified the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Este trabalho propõe um controle preditivo robusto do tipo finite control set para acionamento de máquinas de indução, com controle de corrente no referencial dq alinhado ao fluxo do rotor da máquina. A robustez do controle é levada em consideração na modelagem do controlador e na escolha da orientação, que é feita de forma direta -- estimador Gopinath -- e indireta, para verificar a compatibilidade dos métodos de orientação com o controlador preditivo robusto por estados finitos proposto. Resultados experimentais do controle de corrente mostram robustez para variações nas resistências muito acima do esperado em uma aplicação prática, sem perder desempenho para até a 300% de erro de resistência, mantendo erros de regime abaixo de 5% e o tempo de acomodação na casa de milissegundo. Resultados do controle do velocidade (utilizando uma malha externa com PI) mostram baixo erro de regime e robustez para até 100% de variação de resistência para ambas estratégias de orientação, com destaque para o desempenho da orientação indireta na resposta dinâmica.

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.

Aiming at reducing computational complexity in the multi-step model predictive torque control (MPTC), a novel simplified multi-step MPTC algorithm (P-MPTC) was proposed. Firstly, a multi-step MPTC model was established, and the computational burden of the traditional MPTC (T-MPTC) was discussed. Secondly, based on the MPTC with simplified voltage set (S-MPTC), a simplified MPTC with voltage holding approach (P-MPTC) was proposed, which avoided enumerating all the voltage vectors in candidate set to simplify the computation. Finally, the proposed MPTC algorithm was compared with T-MPTC and S-MPTC to analyze the differences of control performance and computational burden. The result shows that, the proposed simplified algorithm can greatly reduce the computational complexity of multi-step MPTC while the control performance almost not change. For example, in the six-step MPTC, the computational burden in the proposed simplified algorithm is only 0.02% of that in the traditional algorithm, which verifies the effectiveness of the proposed algorithm.

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.

Hierarchical linear control scheme is widely used in ac microgrids. However, its transient response is slow and parameter tuning is time-consuming. Finite Control Set-Model Predictive Control (FCS-MPC) strategy has desired dynamic performance. Nevertheless, it requires an additional sensor to measure the inductor current. This article aims to mitigate these problems by introducing an improved FCS-MPC strategy for paralleled Voltage Source Inverters (VSIs). A capacitor current estimator is employed to reduce the extra current sensor in each VSI. The proposed control scheme consists of two loops: voltage reference generation loop and voltage tracking loop. The voltage reference generation loop achieves accurate load power sharing using virtual impedance-based droop control. Thus, communication is unnecessary among parallel VSIs. The voltage tracking loop utilizes a modified FCS-MPC block with capacitor current estimator to regulate the VSI output voltage. In order to verify the concept of the proposed control strategy, an ac microgrid consisting of two paralleled VSIs is implemented in dSPACE DS1202 hardware-in-the-loop platform. Then a single VSI hardware prototype is implemented and tested experimentally. The proposed method has the merits of good extensibility, low system cost and compact structure. Its steady-state performance is competitive with hierarchical linear control, while the transient response is significantly improved.

In this, work the finite control set (FCS) model predictive direct current control strategy with constraints, is applied to drive three-phase induction motor (IM) using the well-known field-oriented control. As a modern algorithm approach of control, this kind of algorithm decides the suitable switching combination that brings the error between the desired command currents and the predicated currents, as low as possible, according to the process of optimization. The suggested algorithm simulates the constraints of maximum allowable current and the accepted deviation, between the desired command and actual currents. The new constraints produce an improvement in system performance, with the predefined error threshold. This can be applied by avoiding the switching combination that exceeds the limited values. The additional constraints are more suitable for loads that require minimum distortion in harmonic and offer protection from maximum allowable currents. This approach is valuable especially in electrical vehicle (EV) applications since its result offers more reliable system performance with low total harmonics distortion (THD), low motor torque ripple, and better speed tracking. [ABSTRACT FROM AUTHOR]

Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER). The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power; so that both power components (P and Q) are well controlled to their reference values. In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag. The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of formation collision avoidance for unmanned surface vehicles (USVs). Compared with the generalship formation, the formation collision avoidance system (FCAS) needs better responsiveness and stability because of faster speed and smaller volume for USVs. A method based on finite control set model predictive control is proposed to solve this problem. The novelty of the method is that it can control formation quickly to avoid obstacles and reach the destination in accordance with the dynamics of each vessel in the formation, without the prior knowledge of the environment and reference trajectory. The thruster speed and propulsion angle of the USV form a finite control set, which is more practical. The FCAS adopts the leader-follower structure and distributed control strategy to ensure that the followers have a certain autonomy. The first two simulation tests verify that the system has the formation stability, formation forming ability, and the applicability in restricted water. The last simulation test shows that the system can control the USV formation to sail quickly and safely in complex sea scenarios with formation transformation task and multiple dynamic obstacles.

The conventional model predictive control (MPC) suffers from high common-mode voltage (CMV) magnitude and large current ripples. In this article, the reduced CMV MPC strategy with double vector (RCMV-MPCDV) is proposed for three-level inverters, which adopts double vector to reduce the CMV and current ripples simultaneously in per-sampling period. First, based on geometric relationship of median, a novel cost function that evaluates median line distance is proposed to reduce the computational complexity, rather than evaluating the closest distance. Second, in order to reduce the computational burden, the double vector preselection algorithm is presented with fewer evaluation times. Third, the candidate vectors are reclassified and regrouped from partial basic vectors, which restricts CMV magnitude within one-sixth of dc-link voltage. In addition, to select more satisfactory double vectors for RCMV-MPCDV scheme, two improved methods are proposed to compensate the losing candidate vectors. The simulation and experimental results with steady-state performance and dynamic response validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new model predictive control scheme, optimized switching finite control set model predictive control, is studied in this article for neutral-point-clamped (NPC) single-phase three-level pulsewidth modulation (PWM) rectifiers. To ensure the actual stability of the power converter, the ac-side current error of single-phase PWM rectifier and the neutral point voltage of the converter are both converged to the bounded invariant set, and the adjustment of system performance is thus realized by setting the bounded invariant set. Based on the NPC single-phase three-level PWM rectifier model, the ac-side current, the neutral point voltage, and the current error in the future time can be predicted in advance. The above objectives are achieved by optimizing the selection of switch combinations. The advantage of the proposed method is that it can reduce the average switching frequency of the converter when the ac-side current error and the neutral point voltage of the converter are controlled within a certain range, which makes it suitable for the application of high-power single-phase PWM rectifier. [ABSTRACT FROM AUTHOR]

This article proposes a new fault-tolerant control strategy based on finite-control-set model predictive control (FCS-MPC) for interior permanent-magnet synchronous motor (IPMSM) drives under open-circuit faults, including single-phase and double-phase faults. In particular, the double-phase fault will cause a big disturbance to the performance of the motor, but there is no research focused on it with FCS-MPC. The key of this method is that a z subspace orthogonal to the fundamental space is proposed to reduce the total current harmonic distortions under the single-phase fault. And the distributions of space voltage vectors under a double-phase fault are established to realize the fault-tolerant operation. On the other hand, most of the research is focused on the fault-tolerant control of $\text{i}_{d}= 0$ , which is not suitable for the IPMSM, due to the ignorance of the reluctance torque. Hence, the proposed strategy is extended into the maximum torque per ampere operation by virtual signal injection, then the amplitudes of phase currents can be reduced and the efficiency of the system can be improved under fault conditions. Compared with the existing fault-tolerant control schemes, the proposed strategy can realize the fault-tolerant operation under open-circuit faults with faster control response and higher operation efficiency, which has been validated by experimental results. [ABSTRACT FROM AUTHOR]

In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the unbalanced circuit structure to modulate the optimal VV using discrete space vector modulation, which improves the current quality in reducing current ripple and THD. The comparative simulations and experimental results validate the effectiveness of the proposed method.

With the development of marine technology, the research of unmanned surface vehicles (USVs) has received wide attention from the world. Designing effective collision avoidance algorithm in dynamic sea environment is necessary for the navigation of USVs. This paper describes a collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV. For small podded propulsion USV, a three-DOFs maneuvering model is established and the parameters of model are determined by the parameter identification method based on field experiments. About the COLREGs rules, the different maneuvering behaviors are summed up according to encounter situation and collision risk assessment. In the CAS, finite control set model predictive control is adopted, and COLREGs rules are taken as an evaluation standard in the quality function. Finally, the system is verified to avoid multiple dynamic obstacles and compliance vessel in the simulation and real environment, and the results of collision avoidance under four encounter situations are described in detail.

This article proposes current control strategy for four-leg two-level voltage source inverters (VSI). This strategy is based on model predictive control (MPC) approach and presents its comparison with finite control set (FCS) model technique. Besides providing a fast dynamic response, proposed technique replaces complex modulation stage and PI controller being used in earlier classical control techniques. Moreover, the proposed methodology selects the switching sequence which reduces tracking error between output and applied reference currents using cost function optimization. This shows the improvement of system response like FCS model technique. However, FCS model technique results in calculation stress and computation burden; resulting computational delay and extra power consumption by the processor. This problem is solved with the help of proposed single predictive technique which makes the inverter more suitable for large time horizon operations and small sampling time instants. The computational delay is reduced up to 6% compared to the case of FCS-MPC controlled inverter. Performance of proposed simplified technique is analyzed and compared with FCS-MPC controlled system with the help of different types of reference signals. This work will boost the industrial application of four-leg two-level VSI by increasing dynamic response and removing complex modulation stage. [ABSTRACT FROM AUTHOR]

This paper presents a finite control set applied to the direct power control for doubly fed induction generator under normal and voltage sag operation. The controller is designed by using the discretized equations of DFIG. In this case, the generator was modeled as function of reactive and active power, rotor voltage vector, and the flux variations. In this way, the future behavior of the rotor voltage is predicted by using the power error, flux variations, and the minimization of the cost function that allows us to select the optimal voltage vector. Hence, the proposed finite control set controller for direct power control will attend the power references under normal and sag operation. The results obtained in an experimental test bench validate the proposed finite control set controller for direct power control for doubly fed induction generator. [ABSTRACT FROM AUTHOR]

For the conservativeness and computational delay of the traditional finite control set model predictive control (FCS-MPC), a multi-step model predictive voltage control method based on finite control set (FCS-MMPVC) with delay compensation method is proposed in this paper, and the proposed method is validated on a three-level three-phase voltage source PV inverter. In each control cycle, the 27 voltage vectors for inverters are evaluated on line based on predictive model to obtain the optimal and suboptimal switching state. The selected switching state is optimal in two control cycle with the proposed method. Considering that the time spent in the actual sampling and calculation process cannot be ignored, the delay compensation is added in FCS-MMPVC algorithm. The simulation and experimental results show that the proposed method has good stability and reliability. Compared with the traditional FCS-MPC algorithm, the proposed method can reduce the difference significantly between the output voltage and the reference under the unbalanced and non-linear load, and the output voltage quality of the inverter is improved. [ABSTRACT FROM AUTHOR]

A new model predictive torque and flux controller is proposed, which controls the electromagnetic torque and rotor (rather than the stator) flux magnitude. Analytical expressions for the weighting factors are derived that ensure that the proposed controller achieves the same closed-loop performance as predictive current control. In particular, the same low current and torque distortions result, without requiring an outer field-oriented control loop. [ABSTRACT FROM AUTHOR]

This paper focuses on the presentation of an effective predictive flux control (PFC) approach for an induction motor (IM) drive. The finite control set (FCS) principle is used in order to select the inverter voltages and thus there is no need to use the pulse width modulation (PWM) strategy. The proposed control approach uses a cost function formulation in which there is no need for tuning the value of the weighting factor which is inherently present in the classic predictive torque control (PTC) approach, and thus the computational burden is effectively reduced. The proposed PFC approach is derived based on the rotor field orientation principle in which the stator flux components are utilized as control variables instead of utilizing the stator current components as adopted in the classic RFO technique. In order to get better-predicted signals with limited noise content, the stator flux is estimated using an effective flux estimator instead of using the machine voltage model. An effective online identification procedure for the stator transient inductance is proposed and analyzed in a systematic manner, which has led to an improvement in the rotor flux orientation, and thus the robustness of the controller is enhanced. The performance of the drive is tested during the normal speed operation and then during the field weakening operation. The obtained results confirm the validity of the proposed PFC control approach and the effectiveness of the identification procedure in achieving correct and precise rotor field orientation which consequently results in improving the overall dynamic performance of the drive. [ABSTRACT FROM AUTHOR]

Finite control set model predictive torque and flux control uses a scalar weight in its cost function to determine the tradeoff between the torque and flux magnitude tracking error terms. This weight strongly influences the current distortions. It will be shown that an optimal weight can be computed algebraically, which minimizes the current distortions. The resulting predictive torque and flux controller achieves current distortions per switching frequency that are very similar to those of a predictive current controller, provided that the corresponding weight on the switching effort is selected appropriately. To this end, a second analytical expression will be derived. [ABSTRACT FROM AUTHOR]

Vienna rectifier is a typical three‐level rectifier with complicated operating constraints. Also, the constraints pose a challenge for designing controllers with good dynamic performance. As predictive control is good at dealing with constraints, an optimal switching sequence model predictive control (OSS‐MPC) strategy for the three‐phase Vienna rectifier is proposed. A proportional–integral controller is designed to regulate the dc‐link voltage. Also, an improved OSS‐MPC method is utilised to control the input currents. Compared to the conventional finite control set model predictive control, it has the extra advantages of improved steady‐state performance, fixed switching frequency, and elimination of weight factors. Simulation and experimental results verify the correctness and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles. [ABSTRACT FROM AUTHOR]

In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper proposes a Lundberg perturbation observer that is seldom used in the FCS model predictive control situations to remove the adverse effects caused by resistance and inductance mismatch. Firstly, the system model is established, and the FCS-MPCC mechanism is illustrated. Based on the machine model, the sensitivity of the control algorithm to the parameter mismatch is discussed. Then, the Luenberger perturbation observer that can estimate the general disturbance arising from the parameter uncertainties is developed, and the stability of the observer is analyzed by using the discrete pole assignment technique. Finally, the proposed disturbance observer is incorporated into the FCS-MPCC prediction plant model for real-time compensation. Both simulation and experiments are conducted on a three-phase SPMSM, verifying that the proposed strategy has marked control performance and strong robustness.

Finite-control-set-based predictive torque control (PTC) method has received more and more attention in recent years due to its fast torque response. However, it also has two drawbacks that could be improved. First, the torque reference in the cost function of the existing PTC method is generated by the proportional–integral speed controller, so torque reference's generation rate is not fast and its accuracy is low especially when the load torque is given suddenly and inertia value is varying. In addition, the variable prediction of the traditional PTC method depends on the system model, which also has the problem of parameter uncertainties. This paper investigates a disturbance observer (DOB)-based PTC approach for induction motor systems subject to load torque disturbances, parameter uncertainties, and time delays. Not only does the speed loop adopt a DOB-based feed-forward compensation method for improving the system disturbance rejection ability and robustness, but the flux, current, and torque predictions are also improved by using this technique. The simulation and experimental results verified the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The existing model predictive control (MPC) algorithm for variable-speed wind turbines (WTs) is using continuous control set and solved by a quadratic programming method. Its main drawbacks are the heavily computational burden and the difficulty to implement. This paper introduces an alternative MPC method by using finite control set, which is used in controlling WTs at the first attempt. To do this, first of all, the WT's nonlinear model is linearized with information provided by a non-standard extended Kalman filter. Secondly, a discrete-time linear model of the system is used to predict the future value of the interested state variable for possible control sets. In view of the fact that control objectives are different within two operation zones partitioned by wind speed, two quality functions are predefined. One quality function evaluates the optimal generator speed tracking error together with the penalty of torque actuator action at below rated wind speed, while the other evaluates the rated generator speed tracking error and the penalty of pitch actuator action at above rated wind speed. Then, the corresponding control set which minimizes the quality function is selected. Finally, some simulation results are demonstrated to visualize the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In applications involving digital control, the set of admissible control actions is finite/quantized. Coupled with state constraints and fast dynamics, explicit model predictive control (EMPC) provides an attractive control formalism. However, the design of data-driven EMPCs with finite admissible control sets is a challenging and relatively unexplored problem. In this paper, a systematic data-driven method is proposed for the design of quantized EMPCs (Q-EMPCs) for time-varying output tracking in nonlinear systems. The design involves: 1) sampling the admissible state space using low-discrepancy sequences to provide scalability to higher dimensional nonlinear systems; 2) at each sampled data point, solving for optimal quantized model predictive control actions and determining feasibility of the intrinsic mixed-integer nonlinear programming problem; and 3) constructing the Q-EMPC control surface using multiclass support vector machines (MC-SVMs). In particular, four widely used MC-SVM algorithms are employed to construct the proposed data-driven Q-EMPC. Extensive testing and comparison among the different MC-SVM algorithms is performed on 2-D and 5-D benchmark examples to demonstrate the effectiveness and scalability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times below 25\;\mu \texts. Our approach is based on the offline estimation of an infinite horizon value function, which is then utilized as the tail cost of an MPC problem. This allows us to reduce the controller horizon to a very small number of stages, while simultaneously improving the overall controller performance. Our proposed algorithm was implemented on a small size FPGA and validated on a variable-speed drive system with a three-level voltage-source converter. Time measurements showed that our algorithm requires only 5.76\;\mu \texts for horizon N=1 and 17.27\;\mu \text{s} for N=2$ , in both cases outperforming state-of-the-art approaches with much longer horizons in terms of currents distortion and switching frequency. To the authors’ knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real time and achieving comparable performance to formulations with long prediction horizons. [ABSTRACT FROM AUTHOR]

In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles.

This paper presents a predictive control strategy for grid-connected cascaded H-bridge (CHB) converters under unbalanced power generation among each converter phase. The proposed controller belongs to the finite-control-set model predictive control (FCS-MPC) family and is designed to extract unbalanced power from each CHB converter phase while providing balanced power to the grid. The key novelty of this strategy lies in the way the unbalanced power generation among the phases is explicitly considered into the optimal control problem. Power balance is achieved by enforcing the CHB converter to work with a suitable zero-sequence voltage component. The proposed predictive controller is directly formulated in the original $abc$ -framework to account for the common-mode voltage. Simulation and experimental results are provided to verify the effectiveness of the proposed FCS-MPC strategy. [ABSTRACT FROM PUBLISHER]