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model predictive control for hydrogen fuel cell-driven multi-level permanent magnet synchronous motor drive

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 557-575页

作者： Cellamuthu, Santhakumar Jayakumar, Vinoth Chokkalingam, Bharatiraja Gopalasami, Ramanathan KSR Coll Engn Dept Elect & Elect Engn Tiruchengode India SRM Inst Sci & Technol Ctr Elect Mobil Dept Elect & Elect Engn Kattankulathur 603203 India

This paper proposes a model predictive control (MPC) strategy for a three-level Permanent Magnet Synchronous Motor (PMSM) drive powered by a Hydrogen Fuel Cell (HFC). The integration of HFC with electric propulsion systems offers a promising avenue for sustainable transportation characterized by high efficiency and low environmental impact. The MPC algorithm optimally manages the power flow between HFC and PMSM drive by predicting future events, with consideration of dynamic interactions and constraints in the system. The proposed MPC strategy was validated through simulation results, which showed a reduction in hydrogen consumption up to 10 liters per minute (lpm) when operating at low speeds, compared to the constant flow of 60 lpm at full power. The system also achieved a nominal motor speed of 1483 rpm at 100 lpm of hydrogen flow, however considering the availability of Oxygen (21%) in the air 50-70 lpm of hydrogen flow will be the optimal. The findings highlight the effectiveness of the MPC approach, resulting in an increase in the HFC stack efficiency under varying motor speed conditions. This research underscores the potential of integrating HFC with three-level PMSM drives, offering a sustainable solution for advanced electric propulsion systems with enhanced energy efficiency and environmental sustainability.

关键词： Electric vehicle hydrogen fuel cell model predictive control permanent magnet synchronous motor three-level inverter

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model predictive control of a single-buoy wave energy converter with coupled constraints and model adaptation

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OCEAN ENGINEERING 2025年 315卷

作者： Li, Demin Wang, Tianyuan Tao, Ji Sharma, Sanjay Borthwick, Alistair G. L. Dong, Xiaochen Shi, Hongda Ocean Univ China Coll Engn Dept Ocean Engn Qingdao 266400 Peoples R China Univ Plymouth Sch Engn Comp & Math Plymouth PL4 8AA England Ocean Univ China Shandong Prov Key Lab Ocean Engn Qingdao 266100 Peoples R China Ocean Univ China Qingdao Municipal Key Lab Ocean Renewable Energy Qingdao 266100 Peoples R China

A novel optimization framework is introduced for a single-buoy wave energy converter (WEC), which integrates model predictive control (MPC) with the boundary element method (BEM). A time-series auto-regressive (AR) model is employed to predict the wave excitation force in the near future for the MPC algorithm, and its accuracy is evaluated. Incorporation of an AR wave prediction model within the MPC framework significantly enhances operational efficiency and reduces computational costs. Application of a quadratic viscous damping correction and quantification of the prediction horizon effectively mitigated model mismatch between the linearized statespace idealization utilized within the optimizer and the actual controlled WEC. Mitigation of model mismatch significantly enhances prediction accuracy and broadens the applicability of the MPC. A sensitivity analysis is performed, and the coupled effects between constraints are examined to determine an optimal performance benchmark. The imposition of constraints on power take-off (PTO) force variation within the MPC framework ensures system resilience, indicating the practical feasibility of the approach. Simulations based on conditions typical of Zhaitang Island, China, suggest that employing an MPC algorithm for the WEC can increase maximum energy capture efficiency by 20% compared to optimal passive control. The MPC is shown to adjust the device behavior in response to varying sea conditions and to optimize performance for each sea state.

关键词： Wave energy converter model predictive control model mismatch Constraint sensitivity analysis

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model predictive control for space robot manipulator modeled by switched systems: A data-driven approach

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OPTIMAL control APPLICATIONS & METHODS 2025年第2期46卷 564-582页

作者： Chen, Qixin Zhu, Yanzheng Zhang, Jian Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

This article investigates the data-driven based model predictive control (MPC) problem for a class of space robot manipulator (SRM). First, the SRM system is modeled as a class of discrete-time switched system to reflect more system characteristics. Second, only the input-state data are used for end-to-end controller design, and the system identification process is avoided. In order to meet the real-time requirements of the manipulator arm angles, a kind of constrained predictive control method is designed to prevent the overheating or damage of SRM motor, optimizing the cost function for each decision cycle under the safe control input constraints. Then, based on the average dwell time method, the asymptotic stability is assured for the presented SRM system. The design conditions for data-driven based MPC are provided in the form of linear matrix inequalities. Finally, the effectiveness and advantage for the developed data-driven based MPC strategy are validated via a case study of SRM system. This article investigates data-driven model predictive control (MPC) for space robot manipulators (SRM). The SRM is modeled as a discrete-time switched system, using input-state data for controller design without system identification. A constrained predictive control method prevents motor overheating while optimizing the cost function within safe limits. The average dwell time method ensures asymptotic stability, and design conditions for the data-driven MPC are provided as linear matrix inequalities. image

关键词： average dwell time data-driven approach model predictive control space robot manipulator switched systems

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model predictive control with thermal constraints for fuel cell hybrid electric vehicle based on speed prediction

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OPTIMAL control APPLICATIONS & METHODS 2025年第1期46卷 28-43页

作者： Fu, Jiangtao Fan, Bo Fu, Zhumu Song, Shuzhong Henan Univ Sci & Technol Elect informat Engn Luoyang 471009 Henan Peoples R China

Because of the soft dynamic performance of the fuel cell stack, the battery is usually integrated in the power system in fuel cell hybrid electric vehicles. In this article, a real time energy management strategy considering thermal constraints based on speed prediction with neuron network is proposed. The main principle of the proposed control strategy is to get the future power requirement with model predictive control based on the historic speed information, then optimize the objective, function according to the state variables. The objective function is set to minimize the equivalent fuel consumption of the vehicle and extend the life span of the fuel cell stack based on thermal constraints. Contrasting with the control strategy without thermal constraints under the World Light Vehicle Test Cycle driving cycle, the proposed energy management is 0.9% higher, but the temperature of the fuel cell stack and the battery can be limited within an appropriate range. The total equivalent fuel consumption is 3.9% lower than dynamic programming control strategy, which proves the availability of the proposed control strategy can reduce the equivalent fuel consumption while prolonging the fuel cell stack life span. Hardware in loop (HIL) experiment is implemented to testify the real time application of the proposed control strategy. A real time energy management strategy considering thermal constraints based on speed prediction with neuron network is proposed. The proposed control strategy can keep the temperature of the fuel cell stack and the battery within an appropriate range, meanwhile the strategy can reduce the equivalent fuel consumption while prolonging the fuel cell stack life span. Hardware in loop experiment is implemented to testify the real time application of the proposed control strategy. image

关键词： energy management strategy equivalent fuel consumption fuel cell hybrid electric vehicle (FCHEV) model predictive control thermal constraints

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model predictive control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids

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ENERGIES 2025年第7期18卷 1696-1696页

作者： Lim, Sungmin Lee, Jaekyu Lee, Sangyub Korea Elect Technol Inst Energy IT Convergence Res Ctr 25 Saenari Ro Seongnam Si 13509 Gyeonggi Do South Korea

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder.

关键词： model predictive control energy management system renewable energy distributed energy resources mixed-integer quadratically constrained programming

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model predictive control of feed rate for stabilizing and enhancing biogas production in anaerobic digestion under meteorological fluctuations

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JOURNAL OF PROCESS control 2025年 147卷

作者： Moradvandi, Ali Heegstra, Sjoerd Ceron-Chafla, Pamela De Schutter, Bart Abraham, Edo Lindeboom, Ralph E. F. Delft Univ Technol Dept Water Management Stevinweg 1 NL-2628 CN Delft Netherlands Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands Hoogheemraadschap Rijnland Archimedesweg 1 NL-2333 CM Leiden Netherlands

Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on model predictive control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input-output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amoun

关键词： Anaerobic digestion Mechanistic modeling model predictive control Process control Wastewater treatment process

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model predictive control for polytopic uncertain systems with persistent bounded disturbances under long-distance transmission: a cooperation protocol-based approach

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第6期56卷 1223-1239页

作者： Zhang, Wei Wang, Hao Yang, Zhuoheng Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

This paper is concerned with the robust model predictive control (RMPC) problem for a class of systems with polytopic uncertainties and persistent bounded disturbances in the long-distance transmission environment. The signal transmitted over a long distance is very likely to be destroyed by the channel fading. To address this issue, an amplify-and-forward cooperation protocol (AFCP) is adopted in the forward channel, i.e. from sensor nodes to the remote controller node, to improve the transmission quality. Considering the difficulty of obtaining the system state in practice, the dynamic output feedback control in the framework of RMPC is put forward. With respect to the parameter uncertainty, persistent bounded disturbances, and randomness of the transmission power stored in sensors and the AF relay, the objective function is defined by the mathematical expectation of a quadratic function over the infinite time horizon. Then, based on this establishment, a 'min-max' optimisation problem is readily formulated. Furthermore, the singular value decomposition technique is utilised to mitigate the non-convexity and formulate an auxiliary optimisation problem with the solvability, meanwhile, the sufficient criterion for the mean-square input-to-state stability of the underlying system is obtained. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

关键词： model predictive control long-distance signal transmission amplify-and-forward cooperation protocol persistent bounded disturbances input-to-state stability

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model predictive control of purple bacteria in raceway reactors: Handling microbial competition, disturbances, and performance

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Moradvandi, Ali De Schutter, Bart Abraham, Edo Lindeboom, Ralph E. F. Delft Univ Technol Dept Water Management Mekelweg 5 NL-2628 CD Delft Netherlands Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands

Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer amore cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized model predictive control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria model (PBM) simulates this process, and simulation results demonstrate the control system's effectiveness and robustness.

关键词： Purple bacteria model predictive control Supervisory control Hierarchical decision-making control Override control

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model predictive control for risk-based inspection and maintenance planning of offshore wind turbines

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SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Kong, Siwen Liu, Yan Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn 1037 Luoyu Rd Wuhan 430074 Peoples R China

Offshore wind turbines (OWTs) are highly susceptible to fatigue deterioration under extreme environmental conditions, making optimal inspection and maintenance (I&M) planning critical for their safe and sustainable operation. The dynamic nature of weather and complex deterioration processes challenge the long-term effectiveness of maintenance strategies. Moreover, I&M policies often become outdated when deterioration models are updated. Unlike traditional approaches that rely on a fixed life-span horizon, effective I&M planning must adapt to evolving conditions and varying planning horizons. This paper introduces a model predictive control (MPC) method for adaptive, risk-based I&M planning of OWTs. The closed-loop MPC approach provides flexibility by dynamically adjusting inspection and maintenance strategies based on evolving fatigue risks. We propose an augmented state-space model that captures the impact of probabilistic I&M actions on fatigue failure risks over time. A finite-horizon optimal control problem is then formulated to derive the MPC controller, which balances fatigue risk and I&M costs over the planning horizon. The proposed method is demonstrated using a fatigue-prone OWT component. Results show that the MPC controller effectively manages the trade-off between fatigue risk and maintenance costs while remaining adaptable to different planning horizons and decision-making preferences.

关键词： Inspection and maintenance model predictive control offshore wind turbine planning horizon fatigue damage

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model predictive control-based cooperative lane-changing strategy for connected autonomous vehicle platoons merging into dedicated lanes

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Jiang, Yangsheng Man, Zipeng Xia, Kui Wu, Yunxia Wang, Yi Yao, Zhihong Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data Appl Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Sichuan Peoples R China

Multi-vehicle cooperative trajectory planning has emerged as a paradigm in connected autonomous vehicles (CAVs) technology research. One of the more common scenarios is cooperative lane-changing trajectory planning within CAVs and platoon coexistence environments. This research introduces a platoon-based cooperative lanechanging (PCLC) control strategy, facilitating a platoon optimally merge into another platoon. The strategy encompasses three dynamic traffic states: micro-platoon formation, lane-changing preparation, and platoon state recovery, which are integral to the lane-changing process. Two transition signals connect these states, enabling the smooth transition between car-following and lane-changing states for CAVs. The PCLC strategy is formulated mathematically through a hybrid model predictive control (MPC) system to achieve multiple objectives, including traffic smoothness, driving comfort, and terminal state reachability. The MPC model is optimized using receding horizon optimization, allowing the system to adapt to the dynamic traffic environment. Furthermore, the stability of the MPC system is proven theoretically. To validate the effectiveness of the proposed strategy, a collaborative simulation platform utilizing Python and SUMO has been established. The results show that (1) compared with the individual cooperative lane-changing (ICLC) strategy, this strategy can improve the lanechanging efficiency by 47.5%. (2) It becomes apparent that a positive speed difference between the subject CAVs and the target platoon will significantly affect the lane changing efficiency. In addition, the execution time is increased more than 30 % when the platoon size is more than 5 vehicles. (3) The application of greater weight to the acceleration penalty weight and the reduction of the vehicle's acceleration limitations can mitigate the speed fluctuations, thereby facilitating a smoother traffic flow. This study integrates CAV platooning control with lane changing con

关键词： Connected autonomous vehicles Cooperative lane-change model predictive control Platoon Dedicated lane

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