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predictive control of Output-Current in a Single-Pulse-Operated Switched Reluctance Generator

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第4期72卷 3414-3424页

作者： Verma, Anupam Narayanan, Gopalaratnam Indian Inst Sci Dept Elect Engn Bangalore 560012 India

This article deals with the output-current control of a single-pulse-operated switched reluctance generator (SRG). Typically, a linear proportional integral (PI) controller whose gain is obtained by hit-and-trial or by a tuning method is utilized. However, due to the highly nonlinear nature of single-pulse operated SRG, the performance of the PI controller tuned at one operating point deteriorates at another. This article proposes a nonlinear predictive control algorithm for output-current control of single-pulse operated SRG. In particular, two independent controllers are developed, one controlling the turn-ON angle and the other controlling the turn-OFF angle. These controllers manifest good steady-state tracking, dynamic response, and disturbance rejection. Compared to a PI controller, the proposed controller performs consistently at different operating points without tuning any controller parameter. The proposed control algorithm does not require prior measurement of machine characteristics. It does not require storing any offline computed data.

关键词： Prediction algorithms predictive control PI control Switches Tuning Transfer functions Optimization predictive control algorithm single-pulse mode small-signal analysis switched reluctance generator (SRG) turn-OFF angle controller turn-ON angle controller

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predictive control of a heat pipe-cooled reactor based on a neural network model

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NUCLEAR ENGINEERING AND DESIGN 2025年 436卷

作者： Bai, Xi Huang, Jiajun Chen, Zheyu Sun, Peiwei Wei, Xinyu Xi An Jiao Tong Univ Shaanxi Key Lab Adv Nucl Energy & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Shaanxi Engn Res Ctr Adv Nucl Energy Xian 710049 Peoples R China

The adoption of heat pipes for heat transfer makes the heat pipe-cooled reactor (HPR) with significant time delays, making it difficult for traditional proportional integral derivative control systems to meet the rapid and precise power control demands. Therefore, neural network-based model predictive control is applied for HPR. NUSTER-100 model predictive control system utilizes a feedforward neural network model as the prediction model. By employing numerical optimization algorithms to obtain the optimal control variables, the system effectively overcomes the impact of significant time delays on the control system, achieving rapid and precise regulation. It can effectively suppress the impact of abnormal states on control performance, and ensure that heat pipe-cooled reactor can operate safely and stably under abnormal conditions.

关键词： Heat pipe-cooled reactor Neural network model predictive control Numerical optimization Time-delay system

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predictive control-based flow battery energy storage system for microgrid peak shaving

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第2期25卷 1782-1792页

作者： Chang, Dongxing Minist Emergency Management Big Data Ctr 4Hepingli Dist 9 Beijing 100013 Peoples R China

The incorporation of energy storage systems, particularly vanadium redox flow batteries (VRFBs), is critically significant for the operation of microgrids, facilitating effective peak shaving and load balancing. VRFBs exhibit essential attributes such as high flexibility, fast response times, and prolonged operational lifespans, rendering them particularly advantageous for small-scale microgrid applications. Building upon this foundation, the present study proposes a novel microgrid system that is fundamentally based on VRFB technology. This system integrates biomass gasification and solid oxide fuel cells (SOFCs) as primary power generation sources, thereby ensuring a reliable and consistent electricity supply that is specifically tailored to the energy requirements of rural areas. To enhance the charge-discharge efficiency of VRFBs, a new predictive control methodology is introduced, aimed at maximizing the utilization of energy stored within the batteries. This predictive control approach dynamically modifies battery operations in response to real-time battery conditions and projected load demands. The study also assesses peak-shaving strategies in terms of their effectiveness in mitigating demand peaks and enhancing grid stability. In comparison to traditional fixed control methods, the proposed predictive control strategy demonstrates superior intelligence and adaptability, allowing for seamless adjustments to the fluctuating power needs of microgrid systems. By significantly improving the efficiency of VRFBs and lowering operational costs, this innovative approach has the potential to advance the sustainability and resilience of microgrid infrastructures.

关键词： microgrid energy storage vanadium redox flow battery (VRFB) biomass gasification predictive control

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predictive control Strategy Based Fault-Tolerant Formation control for Fully Actuated Multi-Agent Systems

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-16页

作者： Lu, Yuan Xia, Jingping Shen, Lihua Zhang, Ke Nanjing Univ Aeronaut & Astronaut Coll Automation Engn Nanjing 211106 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100039 Peoples R China

For fully actuated multi-agent systems (FAMASs), this paper presents a fault-tolerant formation control strategy grounded in a distributed predictive control framework. With the aid of the state information of neighbouring agents, a discrete distributed fault estimation observer is devised to accurately estimate actuator faults within the FAMASs. Subsequently, leveraging the high-order fully actuated characteristics of FAMAS and the obtained fault estimation information, a fully actuated system approach is employed to construct a fault-tolerant formation controller, which can mitigate the adverse impacts caused by actuator faults and the nonlinear dynamics. Furthermore, the designed controller incorporates a predictive control element, derived by minimizing a cost function related to the multi-step-ahead prediction of formation errors and control inputs. This approach ensures the stability of the FAMAS formation while optimizing control performance. Finally, the effectiveness of the proposed method is proven through its application to UAV formation.

关键词： Multi-agent systems Fault-tolerant control Fully actuated system approach Formation control predictive control

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Finite-Set Model predictive control With Virtual Medium Voltage Vectors and Eliminated Weighting Factors for 3L-NPC Inverters

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Sun, Qingle Lin, Zifan Wang, Zhifu Iu, Herbert Ho Ching Fernando, Tyrone Zhang, Xinan Beijing Inst Technol Natl Engn Res Ctr Elect Vehicles Beijing 100081 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia

Discrete space vector modulation (DSVM) and weighting factor elimination have been investigated to improve the system performance of finite-set model predictive control (FS-MPC). However, the existing DSVM-based FS-MPC without weighting factors for three-level inverters suffers from high computational burden, low algorithm flexibility, and substantially narrowed operating range. To address these issues, this article proposes a novel DSVM-based FS-MPC with the virtual medium voltage vectors (MVVs), which greatly reduces the current harmonics and computational burden, realizes a flexibly adjustable number of time intervals in DSVM, and eliminates the weighting factor while ensuring balanced neutral-point voltage (NPV) over the full range of operating frequencies and load conditions. The introduced virtual MVVs allow the candidate set to be always narrowed down to the voltage vector closest to the reference vector. A simple judgment scheme is proposed to determine whether a real or virtual MVV should be used, which enables a flexible constraint on the maximum NPV error. Carrier-based implementation is also achieved. Experimental results validate the proposed algorithm.

关键词： Vectors Switches Voltage Inverters Voltage control Sun predictive control Medium voltage Harmonic analysis control systems Discrete space vector modulation eliminated weighting factor finite-set model predictive control high flexibility three-level neutral-point-clamped inverter

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Model predictive control of LC Filter Equipped Surface-Mounted PMSM Drives Using Exact Discretization

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第3期72卷 2369-2379页

作者： Bandy, Kristof Stumpf, Peter Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Automat & Appl Informat H-1111 Budapest Hungary

In this article, a novel method is presented to exactly discretize the state-space model of surface-mounted permanent magnet synchronous machines (PMSMs) that are equipped with an inductive-capacitive (LC) filter to allow the model predictive control of the system. An approach is presented to calculate the matrix exponential and the inverse of the angular frequency dependent state-matrix required for the exact discretization. This enables real-time implementation without using memory extensive lookup tables. The method is compared with the Taylor series up to the third term and bilinear approximations as well. Measurement comparatively verifies the theoretical findings in steady-state at various sampling rates, computational requirements, and prediction accuracy experiments. Furthermore, the robustness of the system to speed reference, load torque, and parameter change transients is also explored.

关键词： Discretization permanent magnet synchronous machine (PMSM) predictive control

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Tractable Data-Driven Model predictive control Using One-Step Neural Networks Predictors

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6740-6751页

作者： Menegatti, Danilo Giuseppi, Alessandro Pietrabissa, Antonio Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert I-00185 Rome Italy

Model predictive control (MPC) is a popular control strategy that relies on the availability of a prediction model to estimate future system trajectories over a finite time horizon. Recently, researchers have introduced Neural Networks (NNs) into the MPC framework for the development of data-driven prediction models. In MPC, the control actions are computed by solving iteratively, at each time-step, an optimization problem subject to state and input constraints. Finding the optimal solution to such a problem is a crucial challenge in the data-driven setting, due to the complexity and black-box nature of data-driven models such as NNs. This paper addresses this challenge by proposing a hierarchical deep NN formed by a set of cascading one-step NN predictors whose combination constitutes an interpretable prediction model over the entire prediction horizon. Thanks to the proposed NN architecture, it is shown that the resulting optimal control problem is tractable, as it can be solved by employing efficient iterative algorithms, and interpretable, so that input and state constraints can be enforced seamlessly. The effectiveness of the proposed method is validated through numerical simulations.

关键词： Artificial neural networks predictive models Optimization predictive control Computational modeling Heuristic algorithms Computer architecture Model predictive control deep neural networks mixed-integer convex programming data-driven control

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A Master-Slave Model predictive control Approach for Microgrids

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2025年第1期40卷 540-550页

作者： Carnielutti, Fernanda Aly, Mokhtar Norambuena, Margarita Hu, Jiefeng Guerrero, Josep Rodriguez, Jose Univ Fed Santa Maria UFSM Campus Santa Maria BR-97105900 Santa Maria Brazil Univ Fed Santa Maria UFSM Power Elect & Control Res Grp GEPOC BR-97105900 Santa Maria Brazil Univ San Sebastian Dept Engn Sci Santiago 30332 Chile Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 30332 Chile Tech Univ Catalonia Ctr Res Microgrids Dept Elect Engn Barcelona 08019 Spain ICREA Barcelona 08010 Spain Aalborg Univ CROM AAU Energy DK-9220 Aalborg Denmark

This article proposes a master-slave finite control set model predictive control (FCS-MPC) for microgrids. To demonstrate it, a microgrid is considered, composed of a master neutral-point clamped (NPC) inverter with a battery energy storage system (BESS) and output LC filter;two slave NPC inverters with photovoltaic (PV) panels and output LCL filters;RL and nonlinear loads. Two modes of operation are proposed for the primary control of the microgrid. In the first, the microgrid is connected to the main grid, and the master and slaves are grid-following inverters. In the second, the microgrid is islanded, and the master is a grid-forming inverter, while the slaves remain as grid-following inverters. To validate the performance of the proposed master-slave FCS-MPC, hardware-in-the-loop (HIL) results are presented for different operational conditions of the microgrid, including grid connection, transition to islanded mode, and load variations. The results demonstrate the good performance of the proposed master-slave FCS-MPC, such as fast dynamic response, multivariable control, and robustness to parametric uncertainties and variations.

关键词： Microgrids Main-secondary Inverters Capacitors Voltage control predictive control Impedance Grid-following inverters grid-forming inverters master-slave control microgrids model predictive control (MPC)

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A Model predictive control Algorithm for Path Tracking Based on Multi-Point Preview Dynamics and Safety Guaranteed Constraint

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 441-453页

作者： Wang, Qian Chen, Qitong Liu, Congzhi Li, Liang Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

This paper presents an innovative path tracking method of model predictive control based on multi-point preview (MPP-MPC) that the original equation of state is augmented by considering a dynamics model of the multiple preview points. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. Hence, an ameliorative Kalman filter is adopted to fuse the information about camera and vehicle dynamics to accurately estimate the road curvature. To ensure lateral control performance, dynamic constraints are defined by the estimated road curvature to guarantee comfort and safety. In the control domain of model predictive control (MPC), the model can be approximated as a linear time-invariant model (LTIM) to reduce the computational complexity. The MPC problem can be transformed into a standard quadratic programming (QP) problem with dynamic constraints of guaranteed comfort and safety. Finally, the complex path tracking problem can be solved by the QP problem. Through lane keeping experiments, it is shown that the tracking accuracy and steering smoothness can be significantly improved by the proposed method.

关键词： Vehicle dynamics Safety Roads predictive control Curve fitting Dynamics Wheels Multi-point preview path tracking model predictive control curvature estimation safety guaranteed constraint

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Anticipatory control on Human-Following Robots Using Online Deep-Model predictive control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第2期72卷 1702-1711页

作者： Gui, Shun Yan, Luximon Hong Kong Polytech Univ Sch Design Hong Kong Peoples R China

Mobile robots face challenges when collaborating with humans in crowded and occluded environments. To tackle this issue, we propose a solution called online deep model predictive control (Deep-MPC) and apply it to human-following robots. Deep-MPC incorporates a 3-D human detector, an online learning transition model, and a data-driven MPC framework. Specifically, the 3-D human detector generates the target's 3-D bounding box, while the transition model predicts future states, enabling anticipatory control. By combining the 3-D bounding box's intersection over union (IoU) and state anticipation, we propose a novel evaluation metric that enhances the following robustness. The data-driven MPC framework optimizes robot actions using the neural network of the transition model, and online learning occurs through autonomous interaction with the environment, eliminating the need for system modeling and controller design. To validate our method, we conducted extensive real-world human-following experiments, demonstrating its superior performance compared to some existing methods, skeleton-based methods, and approaches without Deep-MPC.

关键词： Robots Service robots Neural networks Task analysis Target tracking Robot kinematics predictive control Anticipatory control human-following robot model predictive control (MPC) online learning

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