Finite Control Set Model Predictive Control Strategy Research Articles

Simplified robust two-step model predictive control for permanent magnet synchronous motor

Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.

Common-Mode Voltage Reduction Method Based on Variable Sampling Frequency Finite Control Set-Model Predictive Control for PMSM Drive Systems

In this article, a finite control set-model predictive control (FCS-MPC) with variable sampling time is proposed. A zero-voltage vector appears in the dead time between specific voltage vectors, resulting in an unintentionally large common-mode voltage. Herein, a large common-mode voltage was suppressed, and the load current was controlled using a voltage vector combination that did not cause a zero-voltage vector in dead time. Additionally, to improve the total harmonic distortion (THD) of the load current, the intersection of the predicted current and the command current by all the volage vectors (VVs) in the combination is confirmed. The VV where the intersection occurs is selected as the optimal VV. This optimal VV is applied to the point where the predicted current and the reference current intersect. The applicable range of the sampling time should be selected by considering the calculation time and number of switching. Through the proposed FCS-MPC strategy, not only can the common-mode voltage be limited to within ±Vdc/6, but an improved THD can also be obtained compared to the existing method using fixed sampling. The proposed method was verified through PSIM simulation and experimental results.

An Improved FCS-MPC Strategy for Low-Frequency Oscillation Stabilization of PV-Based Microgrids

With the rapid promotion of photovoltaic (PV) power generation, the local consumption of distributed PV power generation at medium and low voltage levels widely forms PV-based microgrids. The continuous increase in the penetration rate of uncertain PV power generation requires it to actively participate in the frequency and voltage regulation of weak microgrid systems. Finite control set-model predictive control (FCS-MPC) for voltage source converter (VSC) shows remarkable advantages in fast dynamic response and robustness compared with traditional cascaded linear control methods. However, the traditional FCS-MPC method for VSC without considering the DC-link voltage dynamic of the PV generator will face oscillations of power and frequency in PV-based microgrids. In this paper, to suppress the low-frequency oscillation issue caused by PV generations, an improved FCS-MPC (I-FCS-MPC) strategy considering the DC-link dynamics of PV generation is proposed, in which the AC side and DC side of the VSC are comprehensively considered to enhance the dynamic performance of the PV-based microgrids. Moreover, a DC-link voltage regulation mechanism is introduced, enabling the PV generators to operate in the maximum power point tracking (MPPT) mode. Finally, experiments are conducted to verify the effectiveness of the proposed control strategy comprehensively.

Sensorless Model Predictive Control of Single-Phase Inverter for UPS Applications via Accurate Load Current Estimation.

Single-phase inverters with an output LC filter, can generate low distortion output voltages, which are suitable for uninterruptible power supply (UPS) systems. The UPS system provides emergency power in the case of utility power failure, requiring high reliability and clean power. The sensorless control method is actually a soft-sensing technique, that reduces system cost, measurement-related losses, and, especially important for UPS systems, enhances the system reliability. This paper proposes a load current sensorless finite control set model predictive control (FCS-MPC) scheme for a single-phase UPS inverter. A time varying observer is proposed, which offers the accurate estimation for individual components simultaneously in periodic load current signal, without subsequent complex calculations. Compared with another two typical sensorless methods (the low-pass filter and the Kalman filter), the proposed observer-based FCS-MPC strategy has smaller load current estimation error and lower output voltage distortion, under both linear and nonlinear loads. The theoretical analysis is verified through simulation and experiment. A single-phase inverter rapid control prototype (RCP) is set up with the Speedgoat real-time target machine, to confirm the effectiveness of the system.

An FCS-MPC Strategy for Series APF Based on Deadbeat Direct Compensation

Aiming at the delay caused by the phase-locked loop (PLL) and harmonic detection in the traditional control of a series active power filter (SAPF), a deadbeat direct control (DBDC)-based finite control set model predictive control (FCS-MPC) strategy for the SAPF is proposed in this work. Firstly, a reference voltage generation mechanism based on direct control is established, which avoids the delay in harmonic detection, thus improving the dynamic response performance of the system. Secondly, the reference current generation mechanism suitable for the SAPF is derived and established using the deadbeat control (DBC) in the αβ coordinate system, based on which the FCS-MPC system is constructed. It eliminates the complex coordinate transformation and PLL and effectively compensates for the full-frequency harmonic voltage. Finally, the proposed control strategy is verified by a simulation and an experiment. The results suggest that the proposed control strategy can effectively compensate for the load voltage and suppress harmonic distortion for the temporary swell and sag of the grid voltage, sudden load changes, and harmonic distortion conditions.

An Ensemble Regulation Principle for Multiobjective Finite-Control-Set Model-Predictive Control of Induction Machine Drives

Finite-control-set model-predictive control (FCS-MPC) has been widely investigated in the electrical drive systems, thanks to its merits of intuitive concept, straightforward implementation, and fast transient response. Owing to the flexible inclusion of constraints, a combination of weighting parameters is derived in the objective function to balance the relationship between the control targets. However, it is a challenging and time-consuming task to optimize a series of weighting parameters. To cope with this issue, this article proposes an FCS-MPC scheme with an ensemble regulation principle for the removal of all the weighting parameters. On the basis of the dimension reduction of the optimization problem, the ensemble regulation principle initially selects the suboptimal solutions for all the control targets. The optimal solution is determined according to a high consistency with the suboptimal solutions via an adaptive mechanism, which not only achieves a decent performance but also avoids a worst case for all the control criteria. The experimental implementation is conducted on a 2.2-kW induction machine platform, which verifies that the proposed scheme outperforms a group of existing weighting factorless FCS-MPC schemes at both the steady state and the transient state.

Finite-Control-Set Model Predictive Control for Low-Voltage-Ride-Through Enhancement of PMSG Based Wind Energy Grid Connection Systems

Grid faults are found to be one of the major issues in renewable energy systems, particularly in wind energy conversion systems (WECS) connected to the grid via back-to-back (BTB) converters. Under such faulty grid conditions, the system requires an effective regulation of the active (P) and reactive (Q) power to accomplish low voltage ride through (LVRT) operation in accordance with the grid codes. In this paper, an improved finite-control-set model predictive control (FCS-MPC) scheme is proposed for a PMSG based WECS to achieve LVRT ability under symmetrical and asymmetrical grid faults, including mitigation of DC-link voltage fluctuation. With proposed predictive control, optimized switching states for cost function minimization with weighing factor (WF) selection guidelines are established for robust BTB converter control and reduced cross-coupling amid P and Q during transient conditions. Besides, grid voltage support is provided by grid side inverter control to inject reactive power during voltage dips. The effectiveness of the FCS-MPC method is compared with the conventional proportional-integral (PI) controller in case of symmetrical and asymmetrical grid faults. The simulation and experimental results endorse the superiority of the developed FCS-MPC scheme to diminish the fault effect quickly with lower overshoot and better damping performance than the traditional controller.

Data-driven finite control set model predictive speed control of an autonomous surface vehicle subject to fully unknown kinetics and propulsion dynamics

This article investigates the speed control problem of an autonomous surface vehicle (ASV) suffering from fully unknown kinetics and propulsion dynamics. A data-driven finite control set model predictive control (FCS-MPC) strategy is presented for tracking a desired surge velocity. In the learning loop, a data-driven neural predictor is present to learn the unknown input gains, model nonlinearities, and environmental disturbances simultaneously. Filtered data of vehicle states are recorded and used to update the neural network parameters. In the control loop, a pulsewidth-modulation-driven speed controller is developed based on the FCS-MPC technique. The optimal control actions are selected based on the receding horizon optimization with a prediction model. The learning error system of the data-driven neural predictor is proved to be input-to-state stable via Lyapunov analysis. A major feature of the proposed method is that optimal performance can be provided for speed tracking without using any a priori knowledge of model parameters or external disturbances. Simulation results substantiate the effectiveness of the proposed data-driven FCS-MPC strategy for an ASV to track a reference surge velocity.

Model Predictive Control With Constant Switching Frequency for Three-Level T-Type Inverter-Fed PMSM Drives

Finite control set model predictive control (FCS-MPC) shows superior dynamic performance with simple structure for motor drives. However, conventional FCS-MPC method presents unfixed switching frequency, making it intractable for the design of heat sinks and output filters. To address this issue, this article is devoted to developing a new FCS-MPC strategy with constant switching frequency for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The switching frequency regulation is realized by extending the period control technique, where the periods are measured between each rising edge and falling edge of the gate signals. The proposed algorithm can be embedded in the iterative process of the FCS-MPC, which can easily achieve the fixed switching frequency with balanced neutral point potential. The performance of this control strategy is numerically evaluated and experimentally verified by an 11-kW PMSM drive platform.

An improved data-driven based model predictive control for zero-sequence circulating current suppression in paralleled converters

To address the requirement of improved power capacity, reliability and operation, parallel converters have been widely applied in micro-grid. However, the connections between DC and AC sides of different parallel converters may lead to the surge of zero-sequence circulating current (ZSCC), which would result in the deterioration of system functionality. Regarding the stated problem above, this paper proposes an improved data-driven based model predictive control for parallel converters. Specifically, a model-free adaptive control (MFAC), which is based on the measured input/output data, is incorporated into the finite control-set model predictive control (FCS-MPC) framework with multi-objective constraints, thus allowing for the suppression of ZSCC and the improvement of robustness performance. Furthermore, to avoid the tuning of weighting factors, a novel cost function is constructed based on the multi-objective decision making method. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties. Furthermore, by this proposal, the accurate current tracking and ZSCC suppression can be achieved. Finally, the validity of the proposed FCS-MPC scheme is verified by comprehensive case studies.

FCS-MPC Without Steady-State Error Applied to a Grid-Connected Cascaded H-Bridge Multilevel Inverter

Cascaded H-bridge multilevel (CHB-ML) inverters are an attractive alternative for supplying power to ac grids as they have high reliability and offer an acceptable quality of voltage at their output terminals. In order to achieve efficient operation in these CHB-ML inverters, they must work at low switching frequencies. The finite-control set model predictive control (FCS-MPC) scheme is a very intuitive strategy for controlling this type of converter, but traditional FCS-MPC algorithms generally have a steady-state error when operating at low sampling frequencies and/or if there are parameters mismatch in the prediction model, regarding those of the real system. In this article, a grid-connected CHB-ML inverter that uses an improved FCS-MPC scheme is proposed. The proposed strategy eliminates the steady-state error in an MPC operating at low sampling frequencies and maintains correct operation when a change in the control reference occurs. Experimental results from a grid-connected CHB-ML inverter with three units (seven levels) demonstrate the feasibility of the proposal.

Adaptive finite control set model predictive control for three-phase inverters connected to distorted grid with fewer voltage sensors

This paper proposes an adaptive finite control set model predictive control (FCS-MPC) scheme for three-phase inverters connected to distorted grid with fewer voltage sensors. For the distorted grid voltage construction, a composite observer is introduced. The observer is composed of two parts, a phase detection module and time-varying observers. The phase information is obtained by the measured voltage of one phase. Then the other two-phase grid voltage is identified by time-varying observers. The convergence of time-varying observers is guaranteed by a Lyapunov function and the Lasalle invariance principle. Moreover, the outputs of the observer are the DC component and the amplitude of each harmonic, which is helpful to obtain positive components without complex post-processing. Finally, the FCS-MPC scheme with simplified cost function is employed for current tracking in natural (abc) coordinate rather than αβ coordinate. Simulation on Matlab/Simulink and experiments on FPGA platform for the three-phase inverter under distorted grid conditions prove that the proposed control approach can perform well with fewer voltage sensors.

High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

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.

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.

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.

Finite-Control-Set Model Predictive Control for Three-Level NPC Inverter-Fed PMSM Drives With $LC$ Filter

The installment of LC filter between the voltage source inverter and motor terminal can reduce the value of dv/dt considerably, and further mitigate the surge voltage stress on the motor windings during the long-distance transmission. However, the sophisticated controller needs to be designed to solve the additional resonance problems caused by the filter. Conventional active damping (AD) scheme mainly introduce the multiloop feedback based on the analysis in s-domain or z-domain, which is realized with limited bandwidth and high tuning complexity. In this article, the finite-control-set model predictive control (FCS-MPC) is adopted to realize the q-axis current reference tracking for motor and suppress the filter resonance simultaneously. Different from the transfer-function-based AD scheme, the mechanism of resonance generation is revealed in the time-domain, where the cost function of FCS-MPC is designed to directly regulate the multivariable trajectory of the system. Therefore, the proposed FCS-MPC controller expresses the intuitive design principle, a simple structure, and fewer tuning parameters, which could be an alternative to the existing AD scheme. To reduce the number of sensors, the Kalman-filter based state estimation is used by considering the one-step delay of the control input. The cost function is further optimized to a simplified form that suits the implementation of the digital platform, and the lower computational burden is obtained. The experimental results prove that the proposed FCS-MPC scheme can ensure the motor-friendly waveform and overcome the resonance problem effectively. The robustness of the controller to parameter variations of the filter is also verified.

A finite control set model predictive control scheme for single-phase grid-connected inverters

The present article investigates a control scheme for single-phase grid-connected inverter based on the finite control set model predictive control (FCS-MPC) approach. The proposed grid integration scheme provides direct control of the active and reactive power (PQ) injected to the grid from distributed energy resources (DER) composed of a photovoltaic (PV) array as a renewable resource integrated with a battery bank as energy storage. The direct PQ control scheme includes also low voltage ride through (LVRT) capability of the inverter during voltage sag.The optimum inverter switching state is indicated via the employed FCS-MPC algorithm instantaneously such that the actual PQ, injected to the grid, will track the corresponding reference values.During normal operation, the active power, injected to the grid, is set to the maximum possible value, while the reactive power is nil. Under voltage sag, the PQ will be injected to the grid as a function of the percentage of voltage sag based on the existing grid codes and regulations.In single-phase systems, successful application of direct PQ control depends on accurately creating the fictitious orthogonal components of grid current and voltage required for instantaneous power computations. Therefore, the following three different orthogonal signal generation (OSG) methods are utilized in this study in order to create such virtual (quadrature) components: second-order generalized integrator (SOGI), all-pass filter (APF), and quarter cycle phase delay (QCPD). In addition, qualitative and quantitative analyses of the obtained results are involved in this study of the FCS-MPC PQ system in order to compare the three adopted OSG methods. The PSIM® software was utilized in this study for modeling and studying the overall FCS-MPC system and the DER grid integration system.According to the results, the investigated FCS-MPC approach is effective in providing quick response and flexible control of PQ injected to the grid through the use of the three OSG methods. Moreover, the investigated FCS-MPC scheme successfully involves the LVRT option in order to improve the dynamic grid voltage support during the permissible duration of voltage sag following the LVRT profiles and grid codes of many countries.

Robust Multiobjective Model Predictive Control with Computation Delay Compensation for Electric Vehicle Applications Using PMSM with Multilevel Inverter

The Three-Level Neutral-Point-Clamped (3L-NPC) inverter fed Permanent Magnet Synchronous Motor (PMSM) drive is an attractive configuration for high performance Electric Vehicle (EV) applications. For such configuration, due to their high performances, the Finite-Control-Set Model Predictive Control (FCS-MPC) is a very attractive control solution. The FCS-MPC scheme is based on the prediction of the future behavior of the controlled variables using the dynamic model of PMSM and the discrete nature of the 3L-NPC inverter. However, the parametric uncertainties and time-varying parameters affect the FCS-MPC algorithm performances. In this paper, robust FCS-MPC controls based on “dynamic error correction” (DEC) and “modified revised prediction” (MRP) are proposed to improve the FCS-MPC robustness without affecting the controller performances and complexity. The proposed strategies are improved also by multiobjective (MO) algorithm optimization and computation delay compensation. The simulation results included prove the performance in robustness and efficiency of the proposed robust FCS-MPC-DEC.

Lyapunov-based finite control-set model predictive control for nested neutral point-clamped converters without weighting factors

This paper presents a novel finite control-set model predictive control (FCS-MPC) strategy for solving the well-known challenges in predictive control regulated NNPCs, which aims to avoid the tedious online adjustment of weighting factors and to achieve the flexible power regulation. Firstly, a modified cost function, which is inspired by Lyapunov control theory, is formulated from system stability point of view. As such, the designed Lyapunov-based cost function has a good potential to avoid the selection of weighting factors for the conventional FCS-MPC scheme, especially in the presence of multiple control objectives. Next, in order to achieve satisfactory regulation performance, a dynamic reference design technique is introduced as an alternative solution to serve this purpose. Worth to note that the computational complexity will be increased while applying the proposed design methodology to the control of NNPCs. In this sense, a computationally efficient FCS-MPC strategy based on Lyapunov theory is presented here. Therefore, the overall computational burden is significantly decreased while maintaining fast transient performance and flying capacitor voltages balancing. Finally, compared with the state-of-the-art FCS-MPC strategies, the effectiveness and feasibility of the proposed methodology for NNPCs are confirmed through comprehensive simulation and experimental results.

A Novel High-Performance Predictive Control Formulation for Multilevel Inverters

This article proposes a novel high-performance predictive control with long prediction horizons to improve the control performance of the multilevel inverters. The finite control set model predictive control (FCS-MPC) has obtained a lot of attention for power converters due to its advantages of high dynamic performance, multi objective capability, no need for PI regulators and PWM modulators. However, the MPC method requires a high number of computations especially for higher-level power converter topologies due to the existence of a huge amount of switching combinations and redundancies. Real-time searching for the optimal switching state among a large candidate pool at a high sampling rate is sometimes impossible with the standard commercial processors. This limitation also prevents real-time implementation of an MPC for multilevel converters with step prediction more than one. To solve the aforementioned issues, this paper presents a novel high-performance FCS-MPC scheme. The proposed FCS-MPC is reformulated mathematically MPC approach to an l 2 norm optimization problem, which can be solved on-line through matrix theory. Compared with the existing MPC optimization algorithms, the proposed MPC formulation has the advantage of a substantial reduction in computational burden, no need for the weighting factors or cost functions, and thus can operate at long horizon prediction length. The proposed method is verified experimentally on a seven-level CHB inverter prototype with a three-step prediction length to demonstrate the ability and performance of the proposed method for multilevel inverters.