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adaptive model predictive control based on stability index for path tracking of autonomous vehicles considering model error

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第19期238卷 9372-9396页

作者： Cui, Ketian He, Jilong Yao, Xinxin Gu, Xianguang Hefei Univ Technol Sch Automot & Transportat Engn 193 Tunxi Rd Hefei 230009 Anhui Peoples R China

Autonomous vehicle path tracking control based on model predictive control (MPC) still faces some challenges, such as unknown external environment disturbance and parametric modeling uncertainties. For these problems, this paper puts forward an adaptive model predictive control (AMPC) strategy which combines stability index and model error compensation to improve vehicle path tracking and stability performance. Firstly, the model error identification scheme based on radial basis function neural network (RBFNN) is designed to estimate the uncertainties of vehicle parametric model. Through the identification and compensation of model error, the model error during critical maneuvers can be reduced. Secondly, to improve vehicle maneuverability and yaw stabilization performance, the AMPC controller is realized by adaptive weight adjustment based on MPC. The stability index is designed to schedule the weights of MPC optimization objectives. Finally, the effectiveness and robustness of the path tracking control strategy are verified by CarSim&Simulink co-simulation and hardware-in-the-loop (HIL) test. The results illustrate that the AMPC strategy based on stability index considering model error not only enhances the tracking accuracy, but also improves the handling and stability performance of vehicles.

关键词： adaptive model predictive control radial basis function neural network stability index path tracking control autonomous vehicles

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adaptive model predictive control scheme for partially internal thermally coupled air separation column

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RESULTS IN ENGINEERING 2024年 23卷

作者： Hamid, Hamedalneel Babiker Aboh Liu, Xinggao Zhejiang Univ Zhejiang Univ NG Platform Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and costeffective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomial models are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.

关键词： Partially internal thermally coupled air separation column adaptive model predictive control Recursive polynomial model estimator Auto-regressive and exogenous inputs Kalman filter

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adaptive model predictive control for grid-forming converters to achieve smooth transition from islanded to grid-connected mode

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第12期17卷 2833-2845页

作者： Meng, Jianhui Zhang, Zhiyi Zhang, Gengmin Ye, Tairan Zhao, Pengfei Wang, Yi Yang, Jian Yu, Jie North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Baoding Peoples R China State Grid Shijiazhing Elect Power Supply Co Shijiazhuang Peoples R China Taizhou Power Supply Co Taizhou Peoples R China North China Elect Power Univ Mailbox 14619 North Yonghua St Baoding Hebei Peoples R China

One of the main capabilities of grid-forming converters is the ability to operate in both islanded and grid-connected modes. The grid-forming converters work autonomously and behave as an AC voltage source when they are isolated from the bulk power system. In a grid-connected mode, it is necessary for them to offer a set of grid-forming capabilities for enhancing system inertia and strength. A grid-forming converter may experience significant fluctuations in voltage, current, and frequency when the transition occurs. This paper investigates an improved adaptive model predictive control (AMPC) method for grid-forming converters to achieve a smooth transition from islanded to grid-connected mode and balance the contradiction between frequency stability and response speed. The AMPC method uses the angular frequency and phase angle as the performance indicators and introduces the angular frequency variations to adaptively adjust its weight coefficient in the objective function. The implementations and effects of the proposed AMPC method on the grid-forming converter are theoretically evaluated. Compared to the conventional control, the proposed control scheme can not only suppress voltage, current, and frequency fluctuations, but also achieve a smooth transition. Additionally, the frequency control strategy in the islanded mode is improved for better independent operation. Finally, experiment tests on a controller-level hardware-in-the-loop simulation platform are carried out to validate the proposed control strategy for achieving a smooth transition.

关键词： DC-AC power convertors frequency control predictive control smoothing methods adaptive model predictive control grid-forming converter smooth transition

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adaptive model predictive control Based Frequency Regulation for Low-inertia Microgrid 5

Adaptive Model Predictive Control Based Frequency Regulation...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Wang, Weichao Sasaki, Yutaka Yorino, Naoto Zoka, Yoshifumi Bedawy, Ahmed Hiroshima University Graduate School of Advanced Science and Engineering Higashi-hiroshima Japan

ISBN: (纸本)9798350339673

Large amount of renewable energy sources affect frequency stability in microgrid (MG) This paper proposes a novel frequency regulation method based on adaptive model predictive control (AMPC) for MG system considering large disturbances to maintain the frequency stability. The MG system performance is approximated by a simplified first-order lag internal prediction model (IPM), which is applied to AMPC. Parameters of the IPM are estimated and updated by unscented Kalman filter (UKF) in real-time. Therefore, an optimal control operation can be obtained to cope with the MG system frequency problem caused by uncertainty of the disturbances, such as wind turbine (WT) power, photovoltaic (PV). An isolated MG model with high penetration of PV, WT, and battery energy storage system (BESS) is used to verify the performance of the proposed AMPC method under low inertia circumstance. The simulation results show that the proposed method has better performance than the PI controller. Moreover, AMPC including UKF can treat low inertia MG system operation without frequency instability. © 2023 IEEE.

关键词： adaptive model predictive control frequency regulation microgrid system renewable energy source unscented Kalman filter

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Equalization strategy of lithium-ion battery packs under two-level structure: An adaptive model predictive control approach

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JOURNAL OF ENERGY STORAGE 2025年 121卷

作者： Liu, Xinghua Xue, Xinying Ma, Wentao Hasanien, Hany M. Wei, Zhongbao Wang, Peng Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Inconsistency is an inevitable problem of electric vehicle battery packs, which will lead to system performance degradation and increase safety risks. In this article, we propose a two-level equilibrium topology structure for inter-group and intra-group dynamics. The intra-group equilibrium topology is based on Buck-Boost converters, which balance any individual battery within the group by flexibly switching modes. The inter-group topology utilizes a Cuk circuit, which allows for energy transfer between every two adjacent battery groups through a dual-layer switch. Compared with the traditional Buck-Boost topologies, the proposed topology has the advantages of simple modular structure and rapid energy transfer speeds. An adaptive model predictive control (AMPC) balancing strategy is proposed to update and adjust the equalizer current. Compared with the traditional algorithms, this algorithm adopts a virtual output compensation (VOC) method to reduce the impact of model linearization errors, thereby improving system robustness and optimizing control performance. Finally, 12 batteries are randomly selected for different initial state of charge (SOC), which are divided into three groups. By conducting static, charging/discharging and consistency tests, it shows that the proposed strategy possesses excellent balance performance.

关键词： Two-level equalization adaptive model predictive control Battery equalization Equalization efficiency

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Fuzzy adaptive model predictive control and sparse identification for unmanned vehicles

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年 1-18页

作者： Li, Meng Wang, Yijun Gui, Weihua Wuxi Inst Technol Sch Vehicles Wuxi 214121 Jiangsu Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Vehicle operating conditions vary significantly across different road sections and speeds, yet traditional model control methods often lack the ability to adapt to these changing environments, resulting in prediction errors. This paper introduces an advanced motion control technique for unmanned vehicle trajectory tracking, leveraging an innovative approach to model predictive control. We develop a comprehensive dynamic model of the vehicle, incorporating relevant constraints, and propose an error trigger adaptive sparse identification (ETASI) method for efficient parameter identification under diverse operating conditions. Our novel fuzzy adaptive model predictive control (FAMPC) technique substantially enhances the adaptability of classical MPC controllers across a wide range of scenarios. Through rigorous analysis, we demonstrate the iterative feasibility and stability of the proposed system. Extensive numerical simulations and real-world vehicle experiments validate the efficacy of our approach. Results reveal that FAMPC achieves robust trajectory tracking across various operating conditions, significantly outperforming both classical MPC and adaptive MPC (AMPC) in terms of accuracy and adaptability. This research provides valuable insights for the future development of unmanned vehicle trajectory tracking control systems capable of handling complex, dynamic environments.

关键词： adaptive model predictive control Sparse identification Fuzzy control Trajectory tracking Unmanned vehicles

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adaptive model predictive control for Heat Pipe-Cooled Microreactors Under Normal and Heat Pipe Failure Conditions

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NUCLEAR TECHNOLOGY 2024年第12期210卷 2274-2289页

作者： Oncken, Joseph Lin, Linyu Agarwal, Vivek Idaho Natl Lab Instrumentat Controls & Data Sci Idaho Falls ID 83415 USA

Microreactors, a specific class of nuclear reactor, feature a thermal power output of <20 MW, with intended use cases ranging from power production for remote localities and industrial facilities, to military applications, to disaster relief. Because the remote locations of these reactors make repairs difficult, and with continuous power production being essential for the intended use cases, the control system for microreactors should be able to operate or safely shut down the reactor under abnormal conditions (e.g. cases of component failure). The nuclear industry is currently pursuing various microreactor designs, one of which is the heat pipe (HP)-cooled microreactor. A potential failure mechanism in this type of microreactor is individual HP failure. The present work explores the notion that even if a single HP fails, an HP-cooled microreactor may still be controllable in its degraded state. A framework is presented for the stable control of an HP-cooled microreactor system's thermal output power and temperature regulation under both normal and HP failure conditions, using adaptive model predictive control (A-MPC). A-MPC was implemented for its ability to maintain optimal controller performance under changing plant state and system constraints. The complex, nonlinear physical phenomena present in an HP-cooled microreactor make using a physics-based model as the A-MPC controller's internal predictor impractical. Thus, a data-based surrogate predictor model was developed for use under both normal and HP failure conditions. The subject under study is a 37-HP system intended to simulate the HP and core thermal behavior of an HP-cooled microreactor. This system was modeled and simulated in DireWolf, a Multiphysics Object-Oriented Simulation Environment (MOOSE)-based application designed to simulate HP-cooled microreactors. The resulting model was used to generate training data for the data-based predictor model and served as the plant simulator when coupled with the

关键词： Microreactor heat pipes adaptive model predictive control

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adaptive model predictive control for Yaw System of Variable-speed Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 219-224页

作者： Dongran Song Qing Chang Songyue Zheng Sheng Yang Jian Yang Young Hoon Joo the School of Automation Central South Universityand Hunan Provincial Key Laboratory of Power Electronics Equipment and GridChangsha 410083China the School of Energy Science and Engineering Central South UniversityChangsha 410083China the School of IT Information and Control Engineering Kunsan National UniversityKunsan 54150Korea

Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control(MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.

关键词： Wind turbine yaw system wind direction adaptive model predictive control

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adaptive model predictive control of a residential solar-air hybrid heat pump system

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ENERGY CONVERSION AND MANAGEMENT 2024年 321卷

作者： Zhao, Zihao Wang, Baolong Li, Xianting Shi, Wenxing Tsinghua Univ Dept Bldg Sci Beijing Key Lab Indoor Air Qual Evaluat & Control Beijing 100084 Peoples R China

With the increasing adoption of renewable energy in the power grid, the future of building energy systems is transitioning toward a distributed and multi-source energy framework. To significantly alleviate strain on the power grid and enhance the integration of renewable energy sources, it is important to optimize the energy intergation and the use of energy storage. To realize decarbonization in domestic hot water supply, this paper proposed a new residential solar-air hybrid heat pumps water heating system based on a three-fluid heat exchanger. Furthermore, an adaptive model predictive control (AMPC) with online model-updating was developed to realize the demand response control for minimized power payment when the hot water requirement is well satisfied. As a result, the proposed hybrid solar-air heat pump system demonstrates superior performance and flexibility, which outperforms the parallel solar-assisted heat pump system with a 14.5% increase in energy savings and a 23.3% improvement in overall system performance. Moreover, the developed model predictive controller consistently demonstrates 52.5% energy saving and 15.5% cost savings in typical day compared to rule-based control strategies.

关键词： Multi-heat source heat pump Demand response adaptive model predictive control controller adaptability

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A Novel adaptive model predictive control Strategy for DFIG Wind Turbine With Parameter Variations in Complex Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第5期38卷 4582-4592页

作者： Hu, Yingjie Chau, Tat Kei Zhang, Xinan Iu, Herbert Ho-Ching Fernando, Tyrone Fan, Ding Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China Northwestern Polytech Univ Sch Power & Energy Xian 710129 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia

In this paper, a novel adaptive model predictive control (MPC) strategy is proposed for doubly fed induction generator (DFIG) wind turbine (WT), which is integrated into a complex power system, in order to improve the power output tracking precision and dynamic performance. Considering the existence of parameter variations in DFIG, an adaptive parameter estimation method is firstly designed. By analysis of stability, the convergence of the parameter estimation algorithm is rigorously proved. Furthermore, the parameter estimation algorithm is effectively integrated into MPC to realize real-time optimal control of DFIG with the adaptive model. To achieve the rotor side converter (RSC) design based on adaptive MPC, the DFIG model is linearized. In addition, a virtual output compensation (VOC) strategy is adopted to alleviate the impact of model linearization errors on the MPC, especially the variation of the model parameter. The newly proposed adaptive MPC-based RSC is capable of greatly improving the tracking performance, meanwhile taking into account the realistic constraints under various operation conditions. The simulation results demonstrate the effectiveness and superiority of the proposed control method.

关键词： adaptive model predictive control doubly fed induction generator parameter estimation virtual output compensation

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