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A Novel adaptive model predictive control for Proton Exchange Membrane Fuel Cell in DC Microgrids

IEEE TRANSACTIONS ON SMART GRID 2022年第3期13卷 1801-1812页

作者： Liu, Yulin Hu, Yingjie Wang, Yuxuan Chau, Tat Kei Zhang, Xinan Iu, Herbert H. C. Fernando, Tyrone Univ Western Australia Sch Engn Perth WA 6009 Australia Northwestern Polytech Univ Sch Power & Energy Xian 710129 Peoples R China

In this paper, a novel adaptive model predictive control (AMPC) algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to improve its output power tracking performance in DC microgrids. The proposed AMPC algorithm is capable of producing superior PEMFC control performance over the conventional MPC and PI controllers. Compared with PI control, AMPC is able to handle physical constraints, which ensures the safe operation of PEMFC under different conditions. Furthermore, it overcomes the common problem of system model dependence that is shared by nearly all the model-based control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm under various operating conditions and system parameter variations are verified by simulation results.

关键词： Mathematical models Microgrids Voltage control Fuel cells Hydrogen Protons Load modeling adaptive model predictive control proton exchange membrane fuel cell DC microgrid parameter estimation

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adaptive model predictive control for electricity management in the household sector

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷 107831-107831页

作者： Colmenar-Santos, Antonio Munoz-Gomez, Antonio-Miguel Rosales-Asensio, Enrique Fernandez Aznar, Gregorio Galan-Hernandez, Noemi UNED Dept Ingn Elect Elect Control Telemat & Quim Apli Juan Rosal 12 Ciudad Univ Madrid 28040 Spain Univ Leon Dept Ingn Elect & Sistemas & Automat Escuela Ingn Ind & Infomat Campus Vegazana S-N Leon 24071 Spain Ctr Invest Recursos & Consumos Energet CIRCE Parque Empresarial DinamizaAv Ranillas3D Zaragoza 50018 Spain

This paper focuses on the optimisation of electricity consumption in residential buildings. To deal with the increase in electricity consumption, the intermittency of renewable energy generation and grid contingencies, a greater effort is required towards residential management optimisation. A novel adaptive model predictive control algorithm is proposed to achieve this objective. The challenges for this research included recognising and modelling the economic and technical constraints of the sources and appliances and addressing the uncertainties concerning the weather and user behaviour. Data-driven models are developed and trained to predict the user behaviour and buildings. Artificial neural networks and statistical models based on the weighted moving average are proposed to capture the patterns of deferrable and non-deferrable appliances, battery storage, electric vehicles, photovoltaic modules, buildings and grid connections. A dual optimisation method is devised to minimise the electricity bill and achieve thermal comfort. The proposed optimisation solver is a two-step optimisation method based on genetic algorithm and mixed integer linear programming. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method through a set of simulation scenarios. The results of the quantitative analysis undertaken as part of this study show the effectiveness of the proposed algorithm towards reducing electricity charges and improving grid elasticity.

关键词： adaptive model predictive control Home Energy Management System Smart Grid Demand Response Distributed Energy Resources Artificial Neural Network Genetic Algorithm

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adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

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MECHANISM AND MACHINE THEORY 2022年 172卷

作者： Tsokanas, N. Pastorino, R. Stojadinovic, B. Swiss Fed Inst Technol D BAUG IBK Stefano Franscini Pl 5 CH-8093 Zurich Switzerland Siemens Ind Software NV Interleuvenlaan 68 B-3001 Leuven Belgium

Hybrid simulation is used to obtain the dynamic response of a system whose components consist of physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, which are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges since the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system. Therefore, a tracking controller is required to adequately compensate for such time delays. In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on adaptive model predictive control, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics. A parametric virtual case study, encompassing a virtual motorcycle, is used to validate the performance and robustness of the proposed controller. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.

关键词： Real-time hybrid simulation adaptive model predictive control Kalman filter Real-time model identification Dynamic response Actuation dynamics compensation

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Ultra battery application for adaptive model predictive control in wind penetrated power systems

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WIND ENGINEERING 2022年第2期46卷 361-375页

作者： Dar, S. Zahid Nabi CMR Inst Technol Dept Elect & Elect Engn 132 AECS LayoutIT Pk Rd Bengaluru 560037 KA India

This work pertains to utilization of small sized Ultrabattery (UB) in wind penetrated power system for LFC. Matlab level 2 S function coding is used to develop a custom made block for adaptive model predictive control for intelligent applications. Inner loop of UB is genetically tuned to mimic the first order reference system;the tuned storage system is connected with wind penetrated power system;with its voltage loop coupled to a function NACE (new area control error) for. A Staircase disturbance is introduced in wind penetrated power system. Investigation studies carried in MATLAB SIMULINK Environment reflect significant improvement in frequency response and tie power deviation of the system. The small rated UB combined with inertial response from wind farms marks for profitable operation. UB voltage and power response is also detailed and power constraints on power electronic converter are maintained within limits.

关键词： adaptive model predictive control ultrabattery load frequency control area control error new area control error genetic algorithim

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Robustifying adaptive model predictive control for a one-link flexible manipulator using super-twisting integral sliding mode control

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INTERNATIONAL JOURNAL OF DYNAMICS AND control 2022年第6期10卷 1934-1942页

作者： Bakhti, Mohammed Hannane, Aycha Bououlid Idrissi, Badr Moulay Ismail Univ Ecole Natl Super Arts & Metiers BP 4024 Meknes 50000 Morocco

Vibration depreciates positioning accuracy and productivity of flexible manipulators and make their modeling and/or controlling a very demanding task. In this paper, an adaptive model predictive control (AMPC) algorithm is detailed to actively damp a nonlinear one-link flexible manipulator while tracking its rigid body position. The derived controller involves in-loop linearization of the plant, around the actual state and control signal, to adapt the required model over the prediction horizon. The robustness of the proposed control strategy is improved using Super-Twisting Integral Sliding Mode control (STISMC). The proposed controller compensates for the assumed matched uncertainties and external disturbances affecting the nominal plant, so the robustness is guaranteed. The effectiveness of the active vibration control is appraised via numerical simulation carried out using Matlab/Simulink.

关键词： Active vibration control adaptive model predictive control In-loop linearization One-link flexible manipulator Super-twisting integral sliding mode control

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Robust Platoon control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based adaptive model predictive control Approach

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Engineering 2024年第11期42卷 244-266页

作者： Daxin Tian Jianshan Zhou Xu Han Ping Lang State Key Laboratory of Intelligent Transportation Systems Beihang UniversityBeijing 100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&Safety Control Beihang UniversityBeijing 100191China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model ***,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon *** this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon *** effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading *** sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive ***,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain *** also provide theoretical proof of the string stability under this control ***,extensive simulations are conducted to validate our *** simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower ***,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

关键词： Connected autonomous vehicle Mixed vehicle platoon Obstacle collision avoidance Cooperative sensing adaptive model predictive control

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adaptive model predictive control with extended state observer formulti-UAVformation flight

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INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2020年第10期34卷 1341-1358页

作者： Zhang, Boyang Sun, Xiuxia Liu, Shuguang Deng, Xiongfeng Air Force Engn Univ Equipment Management & Unmanned Aerial Vehicle En Xian 710051 Peoples R China Anhui Polytech Univ Anhui Key Lab Elect Drive & Control Wuhu Peoples R China

This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

关键词： adaptive model predictive control disturbance rejection extended state observer multiple unmanned aerial vehicles trajectory tracking

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adaptive model predictive control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第2期13卷 33页

作者： Ezemobi, Ethelbert Yakhshilikova, Gulnora Ruzimov, Sanjarbek Castellanos, Luis Miguel Tonoli, Andrea Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy Turin Polytechn Univ Tashkent Dept Mech & Aerosp Engn Tashkent 100095 Uzbekistan

The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

关键词： energy minimization adaptive model predictive control battery sizing thermal limitation mild hybrid electric vehicle

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A real-time optimization method in multi-terminal DC distribution system based on adaptive model predictive control

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2024年 63卷

作者： Zhao, Zida Peng, Ke Xian, Richang Zhang, Xinhui Shandong Univ Technol Zibo Peoples R China

An efficient measure to eliminate voltage oscillation of a multi-terminal DC distribution system (MTDCDS) is locating oscillation source timely and adjusting its control parameters. Combined with oscillation source judgment indexes (OSJIs), a control parameters optimization method based on adaptive model predictive control (AMPC) is proposed. Specifically, based on the static model, a new adaptive updating mechanism of weighting coefficients is proposed. The mechanism adjusts the weighting coefficients according to the real-time states and OSJIs adaptively. Furthermore, taking the minimum weighted square sum of the voltage tracking error and control parameters increment as objective. Compared with the traditional model predictive control (MPC), the proposed method directly adjusts the control parameters of the oscillation source adaptively. which can significantly improve the system response performance and reduce the control cost. The performance has been evaluated using the MATLAB/Simulink and RT Box experimental platform and the results prove the superiority of the proposed method.

关键词： Multi-terminal DC distribution system Oscillation source judgment indexes Weighting coefficients adaptive model predictive control Voltage oscillation

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Extended Kalman Filter Based adaptive model predictive control of a Flexible Manipulator 14th

Extended Kalman Filter Based Adaptive Model Predictive Contr...

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14th IFAC Workshop on adaptive and Learning control Systems (ALCOS)

作者： Hannane, Aycha Bakhti, Mohammed Idrissi, Badr Bououlid Moulay Ismail Univ Ecole Natl Super Arts & Metiers BP 4024Marjane 2 Beni Hamed 50000 Meknes Morocco

This paper presents the adaptive model predictive controller based on the Kalman filter for a one link flexible manipulator. The Kalman filter is a nonlinear state estimator, used to provide the state feedback for the AMPC model to actively damp the tip vibration while tracking its rigid position. The scope of this study is to evaluate the effectiveness and the robustness of the proposed model. The simulation results show that the Kalman filter is the best choice for flexible manipulator state estimation. Copyright (C) 2022 The Authors.

关键词： Flexible manipulator Active vibration control adaptive model predictive control Extended Kalman filter non additive noise

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