Direct Model Predictive Control Research Articles

A long-horizon move-blocking based direct power model predictive control for dynamic enhancement of DC microgrids

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications.

Long-Horizon Direct Model Predictive Control for Power Converters With State Constraints

Long-Horizon Direct Model Predictive Control for Power Converters With State Constraints

Fixed Switching Frequency Direct Model Predictive Control of Synchronous Reluctance Machines Based on Stator Flux Gradients

Fixed Switching Frequency Direct Model Predictive Control of Synchronous Reluctance Machines Based on Stator Flux Gradients

Direct Model Predictive Control of Fuel Cell and Ultra-Capacitor Based Hybrid Electric Vehicle

Direct Model Predictive Control of Fuel Cell and Ultra-Capacitor Based Hybrid Electric Vehicle

A Fixed Switching Frequency Direct Model Predictive Control for Neutral-Point-Clamped Three-Level Inverters With Induction Machines

A Fixed Switching Frequency Direct Model Predictive Control for Neutral-Point-Clamped Three-Level Inverters With Induction Machines

A move blocking based direct voltage model predictive control to enhance the dynamic performance of DC microgrids containing constant power loads

AbstractHere, a move blocking (MB) based direct voltage model predictive control (DVMPC) strategy is introduced to enhance the dynamic performance of a DC microgrid in presence of constant power loads (CPLs). For this aim, an automatic discrete dynamic model is first developed for a boost converter. Considering the CPL effects on the dynamic model of the boost converter, a model predictive control is then designed to directly regulate the output voltage of the converter. Move blocking strategy is finally integrated into DVMPC in order to extend the prediction horizon, and consequently, dealing with the non‐minimum phase characteristic of the boost converter. The dynamic performance of the boost converter with a CPL load in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is evaluated using MB‐based DVMPC and traditional control strategies. The simulation results conducted using MATLAB/Simulink demonstrate that the proposed approach not only enhances the dynamic performance of the DC microgrid, but also reduces the computational burden on the processor. Moreover, experimental results have been performed to validate the proposed strategy. Finally, the stability analysis of the proposed direct voltage control is provided.

Model-Predictive Control of Multilevel Inverters: Challenges, Recent Advances, and Trends

Model predictive control (MPC) has emerged as a promising control method in power electronics, particularly for multi-objective control problems such as multilevel inverter (MLI) applications. Over the past two decades, improving the performance of MPC and tackling its technical challenges such as computational load, modeling accuracy, cost function design and weighting factors selection have attracted great interest in power electronics. This article aims to discuss the current state of MPC strategies for MLI applications, describing the significance of each challenge with the reported effective solutions. Through this review, the MPC methods are categorized into two groups, direct MPC (without modulator) and indirect MPC (with modulator). The recent advances of each category are presented and analyzed, focusing on direct MPC as the most applied method for MLI topologies. In addition, some of the important concepts are experimentally validated through a case study and compared under the same operating conditions to evaluate the performance and highlight their features. Finally, the future trends of MPC for MLI applications are discussed based on the current state and reported developments.

Selective Harmonic Mitigation: Limitations of Classical Control Strategies and Benefits of Model Predictive Control

Selective harmonic mitigation pulsewidth modulation (SHMPWM) combined with model predictive control (MPC) is a promising approach for grid-connected power converters. SHMPWM can guarantee grid code compliance in steady state, e.g. grid harmonic injection, with a reduced output converter filter, while MPC improves dynamic response and allows grid code compliance in the event of grid transients. This paper presents a survey of the MPC strategies already published in the literature developed for their use with SHMPWM. The existing strategies fall into two categories: direct model predictive control with an implicit selective harmonic mitigation modulator, and direct model predictive control based on finite control set (FCS-MPC). One representative control strategy of each group is compared to each other and to the performance of classical proportional- integral (PI) controllers combined with SHMPWM. The goal is to identify the potential benefits of MPC for grid-connected power converters, and determine the main advantages and limitations of the two selected state-of-the-art control strategies. Their performance is assessed through Hardware-in-the-Loop (HIL) experimental results in terms of real-time implementation, harmonic content grid code compliance, dynamic response and performance under grid transients.

Torque Ripple Reduction of Nonsinusoidal Brushless DC Motor Based on Super-Twisting Sliding Mode Direct Power Control

In this paper, a second-order sliding mode control, based on super-twisting algorithm, is proposed for direct power control of the brushless DC (BLDC) motor. The proposed controller uses a super-twisting scheme that requires only sliding surface information and can handle system uncertainties and external disturbances, well. This scheme can improve the BLDC motor torque ripple by solving the disadvantages of the conventional sliding mode control method, such as the chattering effect and high frequency switching control. This method is simple and robust for the BLDC motor’s biggest challenge, torque ripple, which does not require any voltage and current control loops or complex reference frame transformations. The simulation results of the proposed method are compared with the direct power control (DPC) and model predictive control (MPC) methods, which indicate the superiority of the proposed method in both the steady- and transient-states. Moreover, the motor parameters variation in the tracking of active and reactive power are discussed. In addition, the practical results of the proposed method in both cases of speed and load variation show the effectiveness of this method in reducing power (torque) ripple and current total harmonic distortion (THD) and increasing the system’s efficiency compared to other methods.

Quick Search Algorithm-Based Direct Model Predictive Control of Grid-Connected 289-Level Multilevel Inverter

Multilevel inverters, known for their low switching loss and suitability for medium- to high-power applications, often create a heavy computational overhead for the controller. This paper addresses the aforementioned limitation by presenting a novel approach to Direct Model Predictive Control (DMPC) for a grid-tied 289-level ladder multilevel inverter (LMLI). The primary objective is to achieve perfect inverter current control without enumeration. The proposed control method provides a single best solution without complete exploration of the search space. This generalized method can be applied to any multilevel inverter (MLI), enabling them to be used in the grid-tied mode without the computational burden due to a large number of switching states. The DMPC of LMLI with 289-level output and corresponding 289 control inputs, utilizes a discrete model to predict the future state of the state variable. In order to alleviate the enumeration burden, virtual sectors on a linear scale are introduced, and a general formula is provided to identify the single best state among the 289 states, reducing the time required to find the best optimal state per sampling period. Moreover, the proposed control scheme is independent of objective evaluation.

Fake algebraic Riccati equation applied to model predictive control for doubly fed induction generator direct power control

AbstractModel Predictive Control (MPC) and Direct Power Control (DPC) are widely used in Doubly Fed Induction Generator (DFIG) applications. However, in the context of DPC‐based MPC for DFIG, there has been a lack of literature focusing on the design of the MPC parameters. In this way, this article introduces a novel technique for designing weighing matrices in Direct Power Model Predictive Control using the Fake Algebraic Riccati Equation (FARE). As FARE is associated with the infinite receding horizon, a small receding horizon can be designed without compromising the robustness. Furthermore, this article presents a computationally efficient linear state‐space model for DPC that, when combined with a small receding horizon FARE design, offers a controller with low computational cost and robustness. The proposed theory is validated through experimental results under normal operation, variable speed operation, and parameter variations. Additionally, the proposed controller is compared to other MPC approaches.

A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables

Permanent magnet synchronous generator (PMSG) wind power system with full power rating converter configuration is especially suitable for wind energy applications. Direct model predictive control (DMPC) has led to more possibilities in terms of choice because of its straightforward concept for PMSG wind turbine systems in high-power off-shore wind farms. However, due to complete dependence on the model knowledge, parameter mismatches will seriously deteriorate the system control performances. This work presents a model/parameter-independent predictive control method with a novel mechanism to update current/power variations online. The proposed method makes use of only two measurements from the former intervals and the selected control vectors to estimate all variations of the candidate vectors in the present interval. Benefiting from this updating mechanism, the proposed method is completely independent of the model parameters in the state prediction. However, it still has a very low calculating requirement and smooth current/power variation waveforms. The proposed method is compared with classical DMPC. The results validate that the proposed solution outperforms the classical DMPC with model deviations, with considerably improved robustness.

A Direct Model Predictive Control Strategy With an Implicit Modulator for Six-Phase PMSMs

This article proposes a direct model predictive control (MPC) scheme for asymmetric six-phase permanent magnet synchronous machines (PMSMs), which combines control and modulation in one computation stage. By emulating the switching pattern of space vector modulation (SVM), the MPC problem is formulated as a 4-D current control problem where the switching sequences and instants are computed and directly applied to the inverters. This implicit modulation addresses the issue of a variable switching frequency and spread harmonic spectra of conventional direct MPC methods. Moreover, the effect of the modulation constraints and controller bandwidth on the system performance is investigated as well. To verify the effectiveness of the proposed control strategy, experiments are carried out with an asymmetric six-phase PMSM driven by two three-phase two-level inverters.

Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Induction Motor Drives With Reduced Computational Complexity

This paper proposes a long-horizon direct model predictive control (MPC) with reference tracking for medium-voltage (MV) drives that achieves favorable steady-state and transient behavior. However, as MPC is a model-based method, it is susceptible to parameter mismatches and variations of the machine. Moreover, even though a long prediction horizon significantly improves the steady-state behavior of the drive, it significantly increases the computational complexity of the direct MPC problem, rendering its real-time implementation a challenging—if not impossible—task. Motivated by these shortcomings of long-horizon direct MPC, this paper also aims to address them by enhancing the robustness of the developed control strategy, while keeping its computational complexity modest. To achieve the former, a prediction model suitable for MV drive systems is adopted that facilitates the effective estimation of the total leakage inductance of the machine. For the latter, the objective function of the MPC problem is formulated such that, even though the drive behavior is computed over a long prediction interval, only a few changes in the candidate switch positions are considered. The effectiveness of the proposed modeling, control, and estimation approaches is validated with hardware-in-the-loop (HIL) tests for an MV drive consisting of a three-level neutral point clamped (NPC) inverter and an induction machine (IM).

Improved Model Predictive Direct Power Control for Parallel Distributed Generation in Grid-Tied Microgrids

This research proposes an improved finite control set direct power model predictive control method (FCS-DPMPC) for grid-tie distributed generation (DG). FCS-DPMPC predicts the system outcomes using the system model. During the next sampling time, a voltage vector is defined using the cost function to minimize the power ripple, consequently allowing flexibility for power regulation. Furthermore, the impact of implementing a one-step delay is studied and compensated through a model forecast pattern. In addition, a new two-step horizon technique has been developed to minimize switching frequency and computation burden. Simulation results for single DG and parallel operated DGs in a grid-tie manner confirm the effectiveness of the suggested control strategy, which signifies that this is an appropriate approach for distributed generation in microgrids.

Agile Fixed-Wing UAVs for Urban Swarm Operations

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. Such attributes make these vehicles ideally suited for long-range or high-speed reconnaissance operations and position them as valuable complementary members of a heterogeneous multi-robot team. However, these vehicles have traditionally been severely limited with regards to both vertical take-off and landing (VTOL) as well as maneuverability, which greatly restricts their utility in environments characterized by complex obstacle fields (e.g., forests or urban centers). This paper describes a set of algorithms and hardware advancements that enable agile fixed-wing UAVs to operate as members of a swarm in complex urban environments. At the core of our approach is a direct nonlinear model predictive control (NMPC) algorithm that is capable of controlling fixed-wing UAVs through aggressive post-stall maneuvers. We demonstrate in hardware how our online planning and control technique can enable navigation through tight corridors and in close proximity to obstacles.We also demonstrate how our approach can be combined with onboard stereo vision to enable high-speed flight in unknown environments. Finally, we describe our method for achieving swarm system integration; this includes a gimballed propeller design to facilitate automatic take-off, a precision deep-stall landing capability, multi-vehicle collision avoidance, and software integration with an existing swarm architecture.

Polyphase Permanent Magnet Synchronous Motors Direct Current Model Predictive Control with Long Prediction Horizons

This manuscript presents a novel direct current model predictive control with long prediction horizon for medium-voltage polyphase permanent magnet synchronous motor drives. By utilizing the proposed technique, we can reduce the armature current harmonics and torque ripples, minimize the electrical and mechanical losses, improve the torque and speed rendering quality, and achieve a highly reliable permanent magnet motor drive. The state-space model of a representative polyphase permanent magnet synchronous motor is derived with the armature current and flux linkage jointly chosen as the state-space variables. Leveraging completing the squares, the mixed-integer-quadratic-programming (MIQP) problem can be formulated and efficiently solved, which provides the optimal switching sequence for the IGBT inverter. The proposed control algorithm is also implemented and examined with computer simulation to show the robustness and effectiveness.

Direct Model Predictive Control of Synchronous Reluctance Motor Drives

This paper investigates a finite-control set model-predictive control (FCS-MPC) algorithm to enhance the performance of a synchronous reluctance machine drive. Particular emphasis is placed on the definition of the cost function enabling a computationally light implementation while targeting good transient and steady-state performance. In particular, this work proposes the inclusion of an integral term into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracies. A control effort term is also considered in the formulation of the cost function to achieve a high ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional FCS-MPC for a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives.

Gradient-Based Predictive Pulse Pattern Control of Medium-Voltage Drives—Part I: Control, Concept, and Analysis

This article proposes a control and modulation strategy for medium-voltage (MV) drives that exhibits excellent steady-state and transient behavior. Specifically, optimized pulse patterns (OPPs) and direct model predictive control are employed so that the associated advantages of both, such as minimum stator current total demand distortion (TDD) and fast transients, respectively, are fully exploited. To do so, the current reference trajectory tracking and modulation problems are addressed in a coordinated manner in the form of a constrained optimization problem that utilizes the knowledge of the stator current evolution—as described by its gradient—within the prediction horizon. Solving this problem yields the optimal real-time modification of the offline-computed OPP, which guarantees that very low—and close to its theoretical minimum value—stator current TDD is produced at steady state, and very short settling times during transients. To highlight the effectiveness of the proposed strategy, a variable speed drive system consisting of a three-level neutral point clamped inverter and an MV induction machine serves as a case study.

A Novel Predictive Position Control With Current and Speed Limits for PMSM Drives Based on Weighting Factors Elimination

Conventional predictive position control (PPC) method balances the position, speed and current of permanent magnet synchronous machine with different weighting factors. However, tedious tuning work of weighting factors is needed. To simplify the tuning process, a cost function that includes the system position, and current information is constructed based on deadbeat control method. A novel method based voltage boundary is proposed to limit the current and speed simultaneously, the voltage regions corresponding to current and speed constraints are analyzed and the voltage duty cycle is further modified according to the voltage regions, the complex weighting factor turning work is avoided, the current and speed limits are easy to implement with proposed method and less position error can be achieved. Finally, the proposed method is experimentally compared with a conventional finite-control-set PPC method and a direct speed model predictive control method.