Sequential Model Predictive Control Research Articles

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Predictive Control Co-Design (pCCD): A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive MPC in CCD

Abstract Control co-design, or CCD, offers a promising solution for coordinating plant and control design of complex systems to better meet next generation demands. Most CCD algorithms optimize open-loop control signals that solve the problem with a single horizon, yet yield system designs lacking robustness to uncertainties. Recent work has integrated modern MPC policies into CCD. While this results in systems that are more robust, the recursive nature of receding-horizon MPC is computationally expensive and necessitates a bi-level (nested) optimization process to solve sequential MPC problems over smaller horizons.} In this work, I present a single-level predictive control co-design (pCCD) optimization framework that approximates the solution to optimizing a recursive MPC within CCD within a single optimization horizon without the need for nested optimization. The pCCD framework leverages elements of static gain matrices as decision variables to integrate a predictive controller into the algorithm that approximates the benefits afforded by embedding a MPC policy in CCD. The formulation reduces algorithm computational complexity by optimizing over the entire operating horizon at once while retaining key robustness and constraint-handling advantages of MPC. Through a comparative case study for a dual-tank thermal management system, this work shows the pCCD algorithm yields superior robustness to disturbance uncertainties compared to an analogous open-loop CCD system while converging on an optimal system/control design with a 92% reduction in run time compared to an analogous system optimized using a recursive MPC policy within the same CCD algorithm.

Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential Model Predictive Control Framework

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train dynamics. Considering the inertial lag phenomenon within longitudinal dynamics, this study employs a third-order nonlinear control model to characterize train motion. To cope with the intricate running constraints explicitly, a centralized constrained model predictive (MPC) control problem is formulated for the regulation of the trains. Within the framework of sequential solving approach, a novel distributed model predictive control (DMPC) algorithm is designed based on a distributed train running information exchange mechanism. The algorithm decomposes the centralized MPC problem into a sequence of local problems, allowing for sequential computation and meeting the real-time control requirements. Notably, the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train’s topological neighbors when designing its control policy. The feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

High-Performance Sequential Model Predictive Control of a Four-Level Inverter for Electric Transportation Applications

High-Performance Sequential Model Predictive Control of a Four-Level Inverter for Electric Transportation Applications

Development and evaluation of an online home energy management strategy for load coordination in smart homes with renewable energy sources

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage.

Sequential Model Predictive Control for Grid Connection in Offshore Wind Farms Based on Active Disturbance Rejection

In order to harness a greater share of wind energy resources, offshore wind energy projects are venturing into deep-sea locations. In this progression, the issue of grid integration control becomes increasingly challenging. Traditional Model Predictive Control (MPC) has been introduced in offshore wind energy grid integration control due to its merits, such as not requiring modulators, dispensing with decoupling, incorporating constraint handling, and facilitating online optimization. However, it heavily relies on a model and consequently experiences a substantial loss of control effectiveness in the face of system parameter variations. In light of this, this study presents an active-disturbance-rejection-based three-vector sequence model predictive control approach. This method effectively mitigates the influence caused by changes in system parameters, endowing the system with self-disturbance rejection capabilities and enhancing its fault recovery abilities. The method employs self-disturbance control to track voltage reference values and employs the concept of sequence control to eliminate weighting factors in the cost function. Furthermore, it employs three-vector control to achieve error-free operation. The simulation results confirmed that the proposed method effectively minimizes voltage and power transients. It demonstrated superior control effectiveness and shorter response times, enabling the system to rapidly restore to a stable operational state following disturbances.

Impartial Sequential Model Predictive Control of Parallel T-Type Rectifiers for Power Sharing and Circulating Current Elimination

A parallel T-type rectifier connection is often required to improve the system capacity and stability in high-power applications. However, the parallel rectifier faces the challenges of zero-sequence circulating current (ZSCC) elimination, neutral point (NP) voltage balance, DC voltage stability and power sharing. First, to solve the multi-objective optimization problem of two parallel three-level T-type rectifiers with LCL filters (LCL-3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Rs), a double closed-loop control scheme is adopted. In this scheme, an impartial sequential model predictive control (ISMPC) is proposed as the inner-loop controller for ZSCC elimination and NP voltage balance, and an adaptive droop control with voltage feedforward is designed as the outer-loop controller to achieve DC voltage stability and power sharing for each rectifier based on its capacity. Second, the main influencing factors of ZSCC in LCL-3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Rs are analyzed, and ZSCC elimination is considered as one control objective in ISMPC. Finally, ISMPC is proposed to solve the problem that sequential model predictive control (SMPC) could not select the optimal solution due to the fixed priority and the excessive process only controlled a single objective. The proposed method is tested on a prototype hardware platform of a 10-kW and a 5-kW parallel rectifier. Experiment results demonstrate the superiority of this method over existing methods under several typical scenarios.

Passivity-Based Partial Sequential Model Predictive Control of T-Type Grid-Connected Converters With Dynamic Damping Injection

LCL-filter interfaced three-level T-type converters (LCL-3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C) have been widely used in low-voltage applications due to their elevated power quality. However, there exist several problems. To begin with, the performance of the finite-control-set model predictive control (FCS-MPC) controlled 3LT2C relies on an accurate system model and measurement. Consequently, output performances will be influenced when model parameter mismatches, grid impedance variation, and the dead-time of power devices occur. In addition, the robustness of the FCS-MPC controller may also encounter challenges under disturbances and noise. Further, LCL-filter has the resonance problems in the grid currents, which may endanger the system stability. To solve these problems, a passivity-based (PB) partial sequential MPC (PSMPC) with dynamic-damping (DD) injection (DDPB-PSMPC method), which outperforms FCS-MPC and ensures the asymptotic stability, is proposed. First, the passive output voltage based on the Euler-Lagrange (EL) model is obtained using dynamic damping injection. Second, by embedding passive output voltage into MPC, a passivity-based PSMPC robust controller is designed to enhance its anti-disturbance abilities and achieve resonance suppression. Finally, to avoid the time-consuming of weighting factor selection, a PSMPC that allocates non-conflicting objectives in the same layer is introduced. Experimental results on 10-kW prototype demonstrates its excellent performance over existing techniques.

Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives

The rapid growth of energy demand requires progressive energy generation. This, together with the demand for higher efficiency and flexibility, has promoted the application of power electronics in energy systems. During the past decade, model predictive control (MPC) of power electronics has witnessed significant advancements in both dynamic performance and optimal control of the multi-objective terms. Several of these terms can have equal control priorities, resulting in a symmetrical cost function; however, most objectives have different priorities and require weighting factors to resolve the asymmetry in the cost function. Currently, researchers continue to encounter challenges in the optimal design of weighting factors. Moreover, the relative performance of different techniques that either utilize or avoid the weighting factor are uncertain. Therefore, this study focuses on weighting factor design techniques in the literature as applied to wind/solar energy conversion, microgrids, grid-connected converters, and other high-performance converter-based systems. These are grouped under the heuristic, offline tuning, sequential, and online optimization methods. This study demonstrates that optimal online tuning of weighting factors and sequential MPC methods can both offer improved robustness against parameter uncertainties. In addition, the advantages and limitations of different techniques are highlighted.

Sequential model predictive control for five-level MMC-based multi-terminal HVDC systems

Sequential model predictive control for five-level MMC-based multi-terminal HVDC systems

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

Impact of Sequential Model Predictive Control on Induction Motor Performance: Comparison of Converter Topologies

Finite Set Model Predictive Control (FS-MPC) is a widely used technique in power electronic converter applications. One challenge in FS-MPC implementation is selecting appropriate weighting factors, as there is currently no established methodology for finding the best values. An alternative approach is to consider cost functions without weighting factors, as used by the Sequential Model Predictive Control (SMPC). In this paper, the performance of SMPC applied to induction motors is analyzed. The SMPC strategy involves sequentially evaluating simple cost functions by considering a limited number of available switching states for the power electronic converter. This number is the control parameter of the SMPC. The parameter’s domains and a selection criteria based on THD were established in this investigation. The power converter topologies studied include the Voltage Source Inverter (VSI) and the Neutral Point Clamped three-level (3L-NPC). Simulations performed in PLECS software and Hardware-in-the-Loop (HIL) tests using an RT Box for valid parameters satisfy the characteristics of the classical predictive control, such as good control variables tracking and high dynamic response. For a VSI converter, increasing the control parameter results in reduced harmonic distortion, while for an NPC converter, optimal results are achieved with control parameter values within a specific range.

Multivariable sequential model predictive control of LCL‐type grid connected inverter

The conventional single variable indirect model predictive control (SIMPC) of LCL-type three-level grid- connected inverter (GCI) is to indirectly control the grid current by controlling the output current of the inverter, which usually produces large errors and reduces the system anti-disturbance performance. Although the previously proposed multivariable weighted model predictive control (MWMPC) method of LCL-type GCI can directly control many variables, such as grid current, it requires setting the weight coefficient for each variable and involves a substantial amount of calculation. To due with these problems, this paper proposes a multivariable sequential model predictive control (MSMPC) method for LCL-type three-level GCI. The inverter output current, the filter capacitor voltage, and the grid current are gradually predicted and optimized under the variable control sequence of the LCL-type GCI mathematical model. Under the premise of achieving direct control of the variables above, this method can reduce the number of weighting coefficients and gradually contract the optimization space to reduce the computation required for multivariable prediction and optimization. This paper compares the proposed MSMPC to the conventional SIMPC and MWMPC in steady state, transient state performance, and anti-disturbance performance. The effectiveness of the proposed MSMPC is demonstrated through simulation and experimentation.

Optimal Sector-Based Sequential Model Predictive Control for Current Source Rectifier

Conventional model predictive control (MPC) methods for current source rectifier (CSR) use either a PI controller or weight factor-based methods, which require time-consuming tuning methods to develop an efficient controller. Moreover, the limitations on the bandwidth requirements of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter cause resonances and grid current distortions if a variable switching frequency-based predictive control is used. So, a new fixed switching frequency-based MPC algorithm has been developed to control the load and source current simultaneously, thereby facilitating the weight factor less operation and applying a predefined space vector sequence. Unlike the conventional single cost function methods applied to CSR, the proposed method uses separate cost function for each control variable. Simultaneously, to provide fixed frequency operation, duty ratio-based modulation of grid current control has been applied. A virtual impedance-based active damping has been embedded into the supply current control to suppress the oscillations caused by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter and grid current harmonics. Simulation and experimental results have been supported to validate the improvements in terms of supply current total harmonic distortion (THD), steady state, and transient switching performance.

A Sequential MPC Approach to Reactive Planning for Bipedal Robots Using Safe Corridors in Highly Cluttered Environments

This letter presents a sequential Model Predictive Control (MPC) approach to reactive motion planning for bipedal robots in highly cluttered environments with moving obstacles. The approach relies on a directed convex decomposition of the free space, which provides a safe corridor in the form of an ordered collection of mutually intersecting obstacle-free polytopes and waypoints. These are subsequently used to define a corresponding sequence of MPC programs that drive the system to a goal location avoiding static and moving obstacles. This way, the planner focuses on the free space in the vicinity of the robot, thus alleviating the need to consider all the obstacles simultaneously and reducing computational time. We verify the efficacy of our approach in high-fidelity simulations with the bipedal robot Digit, demonstrating robust reactive planning and trajectory realization amidst static and moving obstacles.

Fault-Tolerant Sequential MPC for Vertical Switch Open-Circuit Fault and ZSCC Suppression for Parallel T-Type Converters

A fault-tolerant control (FTC) is proposed for enhancing the reliability of power electronic systems. The software-based FTC of parallel-connected three-level T-type converters (3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs) for suppressing zero-sequence circulating current (ZSCC) induced by a vertical switch open-circuit fault is investigated. A simplified sequential model predictive control (SSMPC)-based FTC technique is developed. Parallel-3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs and their prediction models are introduced, followed by an SSMPC for the no-fault condition. The model inaccuracies under vertical switch faults are elaborated. For the faulty 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C, two fault-tolerant SSMPC (FT-SSMPC) methods are provided by creating a sequential predictive controller that considers ZSCC suppression and grid current tracking. The control policies for the ZSCC are changed from the standard feedback-free to feedback-based cost function (CF) optimization using the unimproved FT-SSMPC. Furthermore, an improved FT-SSMPC is proposed using a phase-deficient CF, which increases the accuracy of the mathematical relation of the fault condition. After fault diagnosis, the proposed method achieves neutral-point voltage balance and excellent point-of-common-coupling current, effectively suppresses the ZSCC, and increases the control reliability. Finally, experiments demonstrate the effectiveness of the proposed FT-SSMPC under various conditions.

Sequential Model Predictive Fault-Tolerance Control for T-Type Three-Level Grid-Connected Converters With LCL Filters

Recently, fault-tolerance control (FTC) methods have been proposed to further improve the reliability of T-type three-level converters. However, the application of model predictive control in FTCs is rare, and the neutral point (NP) voltage imbalance before an open-switch fault occurs has not been solved. This paper focuses on these two problems and proposes two FTC control methods: sequential model predictive control (SMPC) and sequential model predictive tolerance control (SMPTC). Both methods are implemented by designing a sequential predictive control that first considers the NP voltage balance and then grid current tracking. Through the staggered operation of the two methods, a T-type grid-connected converter with an LCL filter can operate normally after an open-switch fault occurs. Moreover, both methods can achieve the NP voltage balance and excellent grid current outputs, eliminate the time-consuming selection of weighting factors, and reduce the number of calculation loops compared with the traditional model predictive tolerance control, thereby improving the control flexibility. The performance and comparison of the open-switch FTC with respect to the NP voltage oscillations, total harmonic distortion of the grid current, and pole voltage were demonstrated using numerous simulation and experimental results.

Power Losses Reduction of T-Type Grid-Connected Converters Based on Tolerant Sequential Model Predictive Control

Three-level T-type converters (3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C) with inductance–capacitance–inductance filters have high power quality of grid current, particularly in the low-voltage system. However, the switching loss and conduction losses degrade the overall efficiency of 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C. The generated heat due to the losses endangers the reliability of the device and shortens its service life. To relieve the power losses as well as maintain high-quality current output, a low-loss-tolerant sequential model predictive control (LL-TSMPC) is proposed. First, based on the energy loss analysis under switching transitions per commutation, the prediction models of both switching loss and conduction loss are established. Second, the total loss reduction is considered as one control objective in LL-TSMPC. Finally, because multiobjective model predictive control (MPC) requires selecting appropriate weighting factors, complicating the design to solve this problem, TSMPC-based lexicographic optimization is introduced to eliminate the tradeoff factors and simplify the MPC controller design. The proposed method is tested on the hardware platform with the rated power of a 10-kW prototype. The experimental results show that the LL-TSMPC method can effectively reduce the switching losses while maintaining the high power quality of the grid current.

Weighting Factor-Less Sequential Predictive Control of LC-Filtered Voltage Source Inverters

To eliminate the weighting factor tuning effort of typical finite-set model predictive control (FS-MPC), this paper proposes a weighting factor-less sequential model predictive control (SMPC) scheme for LC-filtered voltage source inverters. Two independent cost functions for minimizing capacitor-voltage and inductor-current tracking errors are deployed in a cascaded structure, eliminating the weighting factor. First, the optimal cascade order of the cost function is selected by the internal relationship of two control variables. Then, a graphical method is proposed to determine the optimal number of candidate voltage vectors selected from the first cost function. Moreover, to realize the strict current limitation, the current-constraint term is proved to be included in the voltage-related cost function. Another attractive feature of the proposed SMPC is that a smoother inductor-current starting response can be obtained compared to typical FS-MPC. Simulation and experimental results verify the feasibility of the presented approach.

Cascade Model Predictive Current Control for Five-Phase Permanent Magnet Synchronous Motor

As to the model predictive control for five-phase permanent magnet synchronous motor (PMSM), it is difficult to ensure the optimal control performance by using calculation method to obtain the weighting factor. To solve this problem, a cascade model predictive current control based on the idea of sequential model predictive control is proposed for five-phase PMSM for the first time. Firstly, the principle of selecting the optimal voltage vector by the proposed method is analyzed in detail. Then, combined with the characteristics of five-phase PMSM, the control priority of controlled variables is set for designing two cost function schemes without weight factor. The maximum torque scheme can generate a trapezoidal stator voltage, improving the DC bus voltage utilization rate and the system loading capacity. The minimum harmonic current scheme can reduce the harmonic of stator current, obtaining small system noise and vibration. The experimental results indicate that the proposed method can ensure the optimality of the voltage vector applied. Therefore, the five-phase PMSM obtains good performance under different working operation, such as small torque ripple, fast dynamic response, and small harmonic current.