Finite States Model Predictive Control Research Articles

Speed Measurement Delay and its Influence on IFOC Tuning

The Indirect Field-Oriented Control technique has been in use since the 1960s, with some innovations added to the basic original setting. Although variable-speed drive technology has evolved considerably, basic assumptions remain in place. This paper deals with the problem of sensing the mechanical speed using optical devices. This aspect has not been considered in previous works. To focus the contribution, finite-state model predictive control is used in this paper for stator current control. The outer (speed) loop considers PI control as is usually the case. The experimental results presented here show the need for new tuning methods.

Fuzzy MPPT operation-based model predictive flux control for linear induction motors

This research focuses on a methodology for connecting the linear induction machine (LIM) using model predictive flux control (MPFC) fed by Kyocera solar cells. Additionally, it incorporates a fuzzy logic regulator (FLR) for maximum power point tracking (MPPT) control. The boost converter's characteristics are regulated using MPPT, allowing it to achieve a voltage gain of up to twice the input voltage. The finite state-model predictive flux control (FS-MPFC) is employed to synthesize the three-phase voltage source converter switching states. The paper proposes an improved FS-MPFC strategy for LIMs, eliminating the cost function's need for a weighting parameter. This is achieved by unifying its terms of traditional control methods and reducing the number of calculation steps. The validity and effectiveness of the introduced system are confirmed by extensive simulation results. The findings revealed that the system can accurately extract maximum power from the Kyocera solar cells and track the preset speed values. In addition, it can enhance performance by minimizing the thrust and primary flux under varying speed and load conditions.

Fixed-Switching Frequency Finite-State Model Predictive Thrust and Primary Flux Linkage Control for LIM

The special design of linear induction machines (LIMs) leads to adverse effects caused by the longitudinal and end effects. These effects make the thrust control of the LIMs most attractive because its value decreases sharply with the speed increase. Thus, finite-state model predictive control (FS-MPC) is developed to increase the performance of the LIMs. However, the variable switching frequency is the main drawback of this control. Consequently, the main objectives of this paper are to propose FS-MPC with a constant switching frequency, directly control the linear speed, and overcome the problems resulting from the longitudinal and end effects. Therefore, the proposed FS-MPC is based on the thrust and primary flux linkage (TF) control concept. In addition, the end effect is considered during the modeling of the proposed control method. The proposed FS-MPTFC method has been tested under different working cases using MATLAB/Simulink to check its validity. Parameters of a 3 kW arc induction machine have been used during the simulation results.

Predictive Stator Current Control of a Five-Phase Motor Using a Hybrid Control Set

Finite state model predictive control (FSMPC) of multiphase drives can use an extra number of inverter configurations compared with the three-phase case. This, however, requires more computing power for the optimization phase. The application time of each selected voltage vector (VV) is then increased, which can result in higher harmonic content. Reducing the allowed VVs can speed up the computations, thus ameliorating the current tracking/regulation in different orthogonal subspaces. However, the flexibility offered by the reduced set of VVs is less than that of the full set. Furthermore, a lower sampling time can result in an increase in the switching frequency, especially for some speed-load combinations. This article proposes the use of a hybrid scheme where the set of allowed VVs is not fixed but rather selected on-line according to the actual speed and torque producing stator current which are computed by the outer loop. A five-phase induction machine (IM) is used as a test bed for the proposal, showing improved results with respect to the nonhybrid case.

An FPGA Hardware-in-the-Loop Approach for Comprehensive Analysis and Development of Grid-Connected VSI System

Power electronic converters are used for an efficient and controlled conversion of power generated from renewable energy sources and can interface generated power to the grid. Among available power converters, voltage source inverters (VSIs) have been widely employed for grid-connected applications due to better controllability with higher efficiency. Although various conventional, as well as modern control techniques, have been developed for grid connected VSI system, there is a need to select suitable control technique based on application and control requirements. Hardware-in-the-loop (HIL) is considered as a realistic approach for the development of system and control due to the inclusion of an actual hardware system. In this paper, a HIL approach is adopted for the comprehensive analysis and development of a grid connected VSI system using a field programmable gate array (FPGA). The control techniques must deal with trade-off, based on the features and limitations. Therefore, a grid-connected VSI system is developed considering employment of two different conventional control techniques: hysteresis current control (HCC) and PI-based space vector modulation (PI-SVM), as well as finite state model predictive control (FS-MPC) as a modern control technique for investigation considering different parameters. All three control systems are developed through a digital simulator of Xilinx that is integrated with MATLAB-Simulink, while considering an FPGA based system development and testing through FPGA HIL co-simulation methodology.

Experimental Validation of Direct Predictive Control of Variable Speed Wind Energy Conversion System Based on DFIG

The paper presents the design and the implementation of a direct predictive control of a variable speed wind energy conversion system. The conversion chain uses a Doubly Fed Induction Generator DFIG whereas the control method is based on a Finite States Model Predictive Control FS-MPC. The proposed control method selects the optimal switching state of the two levels back to back power converter that minimizes the cost function, where this optimal voltage vector is applied on the output of the power converter in next sampling time. The proposed predictive control strategy uses only one sample time prediction and it is intuitive since it is very simple for implementation. In order to adjust the measured rotor currents to track their references, the error between orthogonal rotor current components predictions to their computed values used to select the optimal vector and applied on the power converter in rotor side CSR in next sampling time. On other side, based on the error between the active and reactive power prediction and their references of the electrical grid, the predictive algorithm control of the gird side converter CSG kept the Dc-link voltage constant and guarantee that the whole system functioning with unity power factor. The experimental results confirm the advantages of using this structure for wind energy conversion system and the effectiveness of the proposed control strategy.

Application of a Control Scheme Based on Predictive and Linear Strategy for Improved Transient State and Steady-State Performance in a Single-Phase Quasi-Z-Source Inverter.

Z and quasi-Z-source inverters (Z/qZSI) have a nonlinear impedance network on their side, which allows the system to behave as a buck–boost converter in their outputs. The challenges derived from the qZSI topology include (a) the control of the voltage and current on its nonlinear impedance network, (b) the dynamic coupling between the and variables, and (c) the fact that a unique set of switches are used to manage the power at and side of the system. In this work, a control scheme that combines a PWM linear control strategy and a strategy based on finite control state model predictive control (FCS-MPC) is proposed. The linear approach works during steady state, while the FCS-MPC works during transient states, either in the start-up of the converter or during sudden reference changes. This work aims to show that the performance of this control proposal retains the best characteristics of both schemes, which allows it to achieve high-quality waveforms and error-free steady state, as well as a quick dynamic response during transients. The feasibility of the proposal is validated through experimental results.

Control Techniques for a Single-Phase Matrix Converter

The single-phase matrix converter is an AC-AC power topology which consists of six bidirectional switches and it is considered the key unit in cascade or multilevel configurations. In this paper, a comparison between two control techniques is presented, one based on a proportional-integral-derivative control module with a pulse width modulator, and the other known as finite-state model predictive control. Simulation and experimental results are presented and discussed to demonstrate the feasibility and performance of both techniques.

Demand Side Management Strategy for Distribution Networks Volt/Var Control: A FCS-Model Predictive Control Approach

Due to the advent of distributed energy resources at distribution level, utility companies face new challenges in terms of voltage control. Existing works in the literature have proposed the use of demand side management (DSM) to provide ancillary services to the grid. Some works explored the use of thermal inertia of buildings to tackle the frequency regulation problem. Existing DSM strategies ignore the potential service loads can provide for the Volt/Var control (VVC) problem. The ability of smart buildings to provide reactive power support to the grid has not been exploited to date. In this paper, we present a finite control state model predictive control strategy for VVC in distribution networks. The robustness of this strategy is validated on a modified IEEE 13 bus system.

Improved Finite-State Model Predictive Current Control With Zero-Sequence Current Suppression for OEW-SPMSM Drives

In order to suppress zero-sequence current (ZSC) for open-end winding surfaced permanent magnet synchronous machine (OEW-SPMSM) drives with single dc voltage source, many methods have been employed. Actual solutions imply either proportional-integral-based control or direct torque control cannot present the characteristic of fast dynamic responses and low torque ripples at the same time. Hence, finite-state model predictive current control (FS-MPCC) based on a cost function with ZSC suppression was proposed. To suppress model parameter mismatch, many previous works have proposed related strategies but few work considered zero-sequence parameter disturbances such as zero-sequence inductance, resistance, and rotor flux linkage disturbances. This article improves the ZSC prediction model based on the previous ZSC errors and zero-sequence voltage deviations, and at the same time ZSC is reduced. A comparison between the conventional FS-MPCC and the improved one is carried out in simulation and experiment to verify the effectiveness of the proposed FS-MPCC scheme.

Particle Swarm Weighting Factor Optimization for Predictive Control of Three Level Inverter with Balanced Voltages

The paper presents a design procedure of weighting factor optimisation for finite states model predictive direct torque and flux control of an induction machine (IM). The multilevel converter feeding the machine is a three level neutral point clamped voltage source inverter (3LNPC-VSI). The flexibility of the predictive method permits to control simultaneously the torque, the flux and the DC link input voltage of the source. The cost function uses at least one weighting factor adjusted to satisfy desired performance. The chosen value of this factor may not be suited for all ranges of operating points and finding these values are very time consuming and complex. The main aim of this paper is to propose an online optimisation of the weighting factor by the particle swarm optimisation (PSO) approach well known by its robustness and fast convergence to the global optimum. Simulation results show that PSO strategy is very efficient to design accurately and quickly these weighting factors.

Selective finite‐states model predictive control of grid interfaced three‐level neutral point clamped photovoltaic inverter for inherent capacitor voltage balancing

A selective finite states model predictive control is proposed for a grid interfaced three-level neutral point clamped solar photovoltaic inverter. The proposed control approach eliminates the weighting factor selection for dc-link capacitor voltage balancing and reduces the computational burden for real-time implementation. The switching states required for the prediction and objective function optimisation are selected based on the position of reference voltage vector in the space vector plane, inverter current directions and the charge status of the dc-link capacitors. As a result, the selection of optimal switching state is fast, easy to implement and eliminates the selection of weighting factor for capacitor voltage balancing. The feasibility of the proposed control approach is verified through simulation and laboratory-scale experimentation. The results confirm that the proposed method attains the inherent dc-link capacitor voltage balance and also retains the dynamic and steady-state current tracking in comparison with the classical finite control-set model predictive control.

Multi-objective fuzzy-decision-making-based FS-MPC with improved performance for grid-connected converters

In this paper, a multi-objective fuzzy-decision-making-based (MO-FDM) finite-state model predictive control (FS-MPC) method is proposed for improving both steady-state and transient-state operations of the grid-connected converters (GCCs). The main objectives of the proposed method are to achieve the excellent steady-state tracking performance and high dynamics without any weighting factors in the designed controller. Firstly, a repetitive control technique is embedded in the predictive controller, and an integral error term is introduced into the standard cost function used in the GCCs. Secondly, a simple and systematic procedure, which relies on MO-FDM technique, is employed to avoid the selection of weighting factors. As such, this scheme has a good potential to avoid the complicated tuning work while the control performance is not affected. Furthermore, in order to enhance the dynamic response and improve the computational efficiency, a dynamic reference design with a simplified cascade-free FS-MPC strategy is embedded into the predictive controller. Finally, simulation results are presented for demonstrating the effectiveness of the proposed control strategy.

Finite‐state model predictive power control of three‐phase bidirectional AC/DC converter under unbalanced grid faults with current harmonic reduction and power compensation

Under unbalanced grid fault conditions, the current harmonic contents of a three-phase bidirectional AC/DC converter increase significantly and twice grid-frequency ripples exist in both active power and reactive power, which influence the output power quality of the converter. Compared with the conventional finite-state model predictive direct power control (MPDPC), the MPDPC with power compensation (MPDPC-PC) method is proposed for the three-phase bidirectional AC/DC power converter to reduce harmonic currents and output power ripples without extraction of complex positive/negative sequences from the grid voltage/current and phase-locked loop. The power compensation values are expressed by grid voltages and their quadrature signals that lag 90 electrical degrees in the αβ stationary coordinates system. MPDPC-PC exhibits better performance by reducing the harmonic contents of grid currents and eliminating active power or reactive power ripples of the three-phase bidirectional AC/DC converter with flexible reactive power compensation capability. Compared with the MPDPC and linear current control schemes, experimental results confirm the effectiveness of the designed method under unbalanced grid conditions.

Finite States Model Predictive Control for Fault-Tolerant Operation of a Three-Phase Bidirectional AC/DC Converter Under Unbalanced Grid Voltages

The bidirectional ac/dc converter is widely used to realize the power conversion between ac and dc microgrid, but the faults of switch devices and unbalanced grid voltages may lead to the decline of power quality and affect normal operation of the converter. The four-switch three-phase (FSTP) fault-tolerant structure is reconstructed from a six-switch three-phase structure with switch device fault. In order to reduce harmonic currents and output power fluctuations under unbalanced grid voltages, finite states model predictive direct power control (MPDPC) with power compensation method is proposed for FSTP structure and predictive power model of the bidirectional FSTP ac/dc converter is established. The power compensation values are expressed by grid voltages and their quadrature signals that lagging 90 electrical degrees in the αβ stationary coordinate system. Compared with the conventional method, phase-locked loop, pulse width modulation, and complex positive-/negative-sequence extraction of grid voltage are not required. Ripples of active power or reactive power under unbalanced grid voltages are eliminated. The proposed fault-tolerant MPDPC with power compensation method ensures the continuous and reliable operation of the bidirectional ac/dc converter with high power quality. Simulation and experimental results are presented to validate the proposed control strategy under symmetrical unbalanced grid voltages with switch device faults.

Modulated Predictive Control for Indirect Matrix Converter

Finite State Model Predictive Control (MPC) has been recently applied to several converter topologies as it can provide many advantages over other MPC techniques. The advantages of MPC include fast dynamics, multi-target control capability and relatively easy implementation on digital control platforms. However, its inherent variable switching frequency and lower steady state waveform quality, with respect to standard control which includes an appropriate modulation technique, represent a limitation to its applicability. Modulated Model Predictive Control (M2PC) combines all the advantages of MPC with the fixed switching frequency characteristic of PWM algorithms. The work presented in this paper focuses on the Indirect Matrix Converter (IMC), where the tight coupling between rectifier stage and inverter stage has to be taken into account in the M2PC design. This paper proposes an M2PC solution, suitable for IMC, with a switching pattern which emulates the desired waveform quality features of Space Vector Modulation (SVM) for matrix converters. The switching sequences of the rectifier stage and inverter stage are rearranged in order to always achieve zero-current switching on the rectifier stage, thus simplifying the current commutation strategy.

Finite-State Model Predictive Current Control for Surface-Mounted Permanent Magnet Synchronous Motors Based on Current Locus

Finite-state model predictive control (FS-MPC) is computationally expensive, as it uses all voltage vectors available for prediction and estimation. This paper proposes a novel finite-state model-based predictive current control (MPCC) scheme to overcome the drawbacks of FS-MPC. A reference frame based on the prediction of the current locus when a zero-voltage vector is employed is established to reduce the computational requirements; specifically, only one zero-voltage vector must be predicted compared with the seven required in conventional FS-MPC. The selection of the optimal voltage vector is based on the direction of the current locus in the established reference frame instead of a cost function, which is necessary and time consuming in conventional FS-MPC. Zero-voltage vectors are also selected by contrasting the distance between the reference and predictive currents to reduce the torque ripple of the proposed method. Simulation and experimental results are presented and confirm the efficient performance of the proposed MPCC.

Multiphase rotor current observers for current predictive control: A five-phase case study

The use of multiphase drives has gained importance in recent times due to some advantages that they provide over conventional three-phase ones. High performance stator current control can be achieved by means of direct command of voltage source inverter. In this context finite-state model predictive control is a very flexible strategy that has been recently proposed and analyzed. Nevertheless, its implementation must solve the problem of estimating rotor quantities, being the conventional solution a simple backtracking procedure. In this respect, observers appear as an attractive alternative. However, while they have been used with FOC, sensorless drives and for fault detection, they have not been used yet for predictive control of drives as a way to provide rotor values estimates. In this paper the authors propose to incorporate a full-order rotor current observer in a finite-state model predictive controller of a five-phase induction machine. Pole placement design based on Butterworth filters is used. The new estimation scheme and the standard procedure are compared. By means of experimental tests, the differences between both approaches and the benefits of including a rotor observer are illustrated and verified.

A computationally efficient finite state model predictive control for cascaded multilevel inverter

Abstract This paper investigates a new scheme for Finite State Model Predictive Control fed five level Cascaded Multilevel Inverter. This proposed method uses 19 voltage vectors out of totally 125 voltage vectors of the Cascaded Multilevel inverter. Since this inverter has many switches, reducing the average switching frequency is the matter of great importance for high voltage applications, as they contribute to significant switching losses. Two schemes of Finite State Model Predictive Control (FSMPC) are proposed i.e. FSMPC1 employing the current control of the Cascaded Multilevel inverter with less (19) voltage vectors and FSMPC2 aiming the control of inverter load current as well as reducing the average switching frequency using 19 vectors. The performance of the proposed schemes is compared with conventional 61 voltage vector scheme of the Cascaded multilevel Inverter. The results show that both the proposed schemes perform well for steady state and dynamic operating conditions.

Harmonic Distribution in Finite State Model Predictive Control

Finite State Model Predictive Control (FSMPC) has recently been proposed for control of electrical systems, including drives for induction machines (IM),permanent magnet machines and converters of various types. The FSMPC does not need modulation elements such as PWM, enabling a fast control. Also it provides ease of implementation in different settings. However, the problems associated with FSMPC have not been analyzed in detail. This paper brings to the surface some inconvenient traits, in particular, the problem of harmonics is exposed and analyzed, showing its distribution. The findings are discussed and their relevance is put into context, showing their interdependence with various practical aspects such as modulation index, operating frequency and losses.