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A composite robust adaptive control for double-fed variable-speed pumped storage units considering stochastic uncertainty

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Energy 2025年 329卷

作者： Wang, He Tan, Xiaoqiang Li, Chaoshun Presas, Alexandre School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Digital River Basin Science and Technology Huazhong University of Science and Technology Wuhan430074 China Institute of Water Resources and Hydropower Huazhong University of Science and Technology Wuhan430074 China ETSEIB Av. Diagonal 647 Barcelona08028 Spain

As an advanced technology in pumped storage, the doubly-fed variable-speed pumped storage unit (DFIM-VSU) enhances flexibility, overcomes conventional constraints, and emerges as a transformative solution for power systems. However, its implementation faces challenges, including dynamic uncertainties, complex couplings, and control limitations. To address these issues, this paper proposes an adaptive control strategy to mitigate stochastic uncertainties and suppress composite disturbances, enhancing the safety of DFIM-VSU regulation. First, a coupled VSU governor-excitation model with parameter perturbations is developed. Leveraging probability density theory, a framework for dynamic response quantification is established, highlighting the regional probability distribution of key states. Building on this, a novel and efficient nonlinear disturbance observer and model reference adaptive composite controller (NDOB-MRAC) is proposed. Through state matrix reconstruction and real-time disturbance feedforward compensation, the controller can effectively optimize overshoot, maximum deviation, and regulation time in unit speed. Experimental results demonstrated that NDOB-MRAC expanded the stable domain and significantly enhanced multi-condition adaptability and dynamic performance. Notably, under low-head, high-load conditions, the proposed method reduced regulation time by 39.8 % compared to PI control while eliminating speed overshoot. These findings clarify operational uncertainties of VSUs and provide a reliable foundation for robust control in pumped storage systems. © 2025 Elsevier Ltd

关键词： model reference adaptive control

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DEVELOPMENT AND IN-SILICO EVALUATION OF A CLOSED-LOOP FLUID RESUSCITATION control ALGORITHM WITH MEAN ARTERIAL PRESSURE FEEDBACK

DEVELOPMENT AND IN-SILICO EVALUATION OF A CLOSED-LOOP FLUID ...

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作者： Alsalti, Mohammad University of Maryland

学位级别：硕士

In this thesis, a model-based closed-loop fluid resuscitation controller using mean arterial pressure (MAP) feedback is designed and later evaluated on an in-silico testbed. The controller is based on a subject specific model of blood volume and MAP response to fluid infusion. This simple hemodynamic model is described using five parameters only. The model was able to reproduce blood volume and blood pressure response to fluid infusion using an experimental dataset collected from 23 sheep and is therefore suitable to use for control design purposes. A model-reference adaptive control scheme was chosen to account for inter-subject variability captured in the parametric uncertainties of the underlying physiological model. Three versions of the control algorithm were studied under different measurement availability scenarios. In-silico evaluation of the three controllers was done using a comprehensive cardiovascular physiology model on a cohort of 100 virtually generated patients. Results clearly show that a tradeoff exists between tracking and estimation performance depending on measurement availability.

关键词： Mechanical engineering adaptive control Blood pressure Closed-loop control Composite adaptive control Fluid resuscitation model reference adaptive control

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Improving the performance of a Printing System using model reference adaptive control: an LMI Approach

Improving the performance of a Printing System using Model R...

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American control Conference

作者： M. Ezzeldin S. Weiland P.P.J. van den Bosch Department of Electrical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands

ISBN: (纸本)9781457710957

A novel model reference adaptive control (MRAC) scheme is proposed. The formulation involves recasting the error dynamics, which comprise of the tracking error and error of the controller parameters, into a Takagi-Sugeno model. Instead of using a single adaptation gain, multiple adaptation gains are employed. A sufficient condition is derived to ensure the asymptotic stability of the system for both state and output feedback cases. The adaptive control problem is formulated as a minimization of the L_2 gain. The optimal adaptation gains are obtained by solving a linear matrix inequality problem. A numerical example compares the proposed approach with the standard MRAC. Moreover, the proposed approach is applied to control a real printing system to improve the printing quality where large parameter variations, owing to different print jobs, and disturbances are presence.

关键词： model reference adaptive control Printing system print quality parameter variation Self tuning Interim Local Management Interface asymptotic stability region Output feedback System stability

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Know3-RAG: A Knowledge-aware RAG Framework with adaptive Retrieval, Generation, and Filtering

arXiv

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arXiv 2025年

作者： Liu, Xukai Liu, Ye Wu, Shiwen Zhang, Yanghai Yuan, Yihao Zhang, Kai Liu, Qi School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China The Hong Kong University of Science and Technology Hong Kong

Recent advances in large language models (LLMs) have led to impressive progress in natural language generation, yet their tendency to produce hallucinated or unsubstantiated content remains a critical concern. To improve factual reliability, Retrieval-Augmented Generation (RAG) integrates external knowledge during inference. However, existing RAG systems face two major limitations: (1) unreliable adaptive control due to limited external knowledge supervision, and (2) hallucinations caused by inaccurate or irrelevant references. To address these issues, we propose Know3-RAG, a knowledge-aware RAG framework that leverages structured knowledge from knowledge graphs (KGs) to guide three core stages of the RAG process, including retrieval, generation, and filtering. Specifically, we introduce a knowledge-aware adaptive retrieval module that employs KG embedding to assess the confidence of the generated answer and determine retrieval necessity, a knowledge-enhanced reference generation strategy that enriches queries with KG-derived entities to improve generated reference relevance, and a knowledge-driven reference filtering mechanism that ensures semantic alignment and factual accuracy of references. Experiments on multiple open-domain QA benchmarks demonstrate that Know3-RAG consistently outperforms strong baselines, significantly reducing hallucinations and enhancing answer reliability. © 2025, CC BY.

关键词： model reference adaptive control

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Online policies for real-time control using MRAC-RL

arXiv

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arXiv 2021年

作者： Guha, Anubhav Annaswamy, Anuradha M. Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

In this paper, we propose the model reference adaptive control & Reinforcement Learning (MRAC-RL) approach to developing online policies for systems in which modeling errors occur in real-time. Although reinforcement learning (RL) algorithms have been successfully used to develop control policies for dynamical systems, discrepancies between simulated dynamics and the true target dynamics can cause trained policies to fail to generalize and adapt appropriately when deployed in the real-world. The MRAC-RL framework generates online policies by utilizing an inner-loop adaptive controller together with a simulation-trained outer-loop RL policy. This structure allows MRAC-RL to adapt and operate effectively in a target environment, even when parametric uncertainties exists. We propose a set of novel MRAC algorithms, apply them to a class of nonlinear systems, derive the associated control laws, provide stability guarantees for the resulting closed-loop system, and show that the adaptive tracking objective is achieved. Using a simulation study of an automated quadrotor landing task, we demonstrate that the MRAC-RL approach improves upon state-of-the-art RL algorithms and techniques through the generation of online policies. © 2021, CC BY.

关键词： model reference adaptive control

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adaptive state-feedback control with sensor failure compensation for asymptotic output tracking

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INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2019年第1期33卷 130-156页

作者： Song, Ge Tao, Gang Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

This paper addresses a new adaptive output tracking problem in the presence of uncertain plant dynamics and uncertain sensor failures. A new unified nominal state-feedback control law is developed to deal with various sensor failures, which is directly constructed by state sensor outputs. Such a new state-feedback compensation control law is able to ensure the desired plant-model matching properties under different failure patterns. Based on the nominal compensation control design, a new adaptive compensation control scheme is proposed, which guarantees closed-loop signal boundedness and asymptotic output tracking. The new adaptive compensation scheme not only expands the sensor failures types that the system could tolerate but also avoids some signal processing procedures that the traditional fault-tolerant control techniques are forced to encounter. A complete stability analysis and a representative simulation study are conducted to evaluate the effectiveness of the proposed adaptive compensation control scheme.

关键词： model reference adaptive control plant-model output matching sensor failure compensation state feedback

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Force ripple compensation in a PMLSM position servo system using periodic adaptive learning control

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ISA TRANSACTIONS 2019年 95卷 266-277页

作者： Zhang, Wenjing Nan, Nan Yang, Yifan Zhong, Weifeng Chen, YangQuan Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Calif Merced 5200 N Lake Rd Merced CA 95343 USA

Force ripple deteriorates the performance of permanent magnet linear synchronous motor (PMLSM) servo systems. Using a model reference adaptive control and periodic adaptive learning control (MRAC-PALC) algorithm, this paper presents a novel compensation method to eliminate the influence of force ripple on the system performance of a position servo system under repetitive motion tasks. The key idea of the proposed method is to utilize the periodic characteristics of both force ripple and system motion. The controller consists of four components: a PD component, a feedforward component, a velocity feedback component and an MRAC-PALC compensator. The first three components are designed in a conventional way. The compensator is divided into two parts: in the 0th-iteration, an MRAC algorithm is employed to obtain the initial information, and in the ith-iteration (i >= 1), a PALC algorithm is used to learn from the information obtained in the previous period and update the controller parameters for estimating force ripple. Moreover, a theoretical stability analysis is given via Lyapunov stability theorem, and some comparative results are provided through simulations and experiments. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： PMLSM position servo system Force ripple compensation Periodic adaptive learning control model reference adaptive control

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Decentralized fault-tolerant MRAC for a class of large-scale systems with time-varying delays and actuator faults

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JOURNAL OF PROCESS control 2019年 75卷 171-186页

作者： Deng, Chao Yang, Guang-Hong Er, Meng Joo Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper considers the decentralized fault-tolerant model reference adaptive control (MRAC) problem for a class of nonlinear large-scale systems with matched unknown nonlinearity and uncertain time delay interconnections and mismatched interconnections. A novel cyclic-small-gain condition is achieved to show the boundedness of tracking errors. Furthermore, by introducing adaptive mechanisms, a new decentralized fault-tolerant controller is developed. In addition, it is shown that the matched unknown nonlinear functions and the matched uncertain time delay functions can be compensated by the developed controller. Compared with existing large-scale systems results, the proposed method is effective to solve the fault-tolerant MRAC problem for large-scale systems with time-varying delays and mismatched interconnections. Finally, the validity is shown by applying the proposed method to a chemical reactor system with two subsystems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： model reference adaptive control Time-varying delay Large-scale systems Sliding mode control Fuzzy logic systems

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A Novel Blended State Estimated adaptive controller for Voltage and Current control of Microgrid Against Unknown Noise

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IEEE ACCESS 2019年 7卷 161975-161995页

作者： Munsi, Md Shahin Siddique, Abu Bakar Das, Sajal K. Paul, Sanjoy Kumar Islam, Md Rabiul Moni, Mohammad Ali Rajshahi Univ Engn & Technol Dept Mechatron Engn Rajshahi 6204 Bangladesh Univ Technol Sydney Business Sch Sydney NSW 2007 Australia Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Univ Sydney Fac Med & Hlth Sydney NSW 2006 Australia Pabna Univ Sci & Technol Dept Comp Sci & Engn Pabna 6600 Bangladesh

In this study, a novel blended state estimated adaptive controller is designed for voltage and current control of microgrid against unknown noise. The core feature of the microgrid (MG) is its ability to integrate more than one distributed energy resource into the main grid. The state of a microgrid may deteriorate due to many reasons, for example malicious cyber-attacks, disturbances, packet losses, etc. Therefore, it is necessary to achieve the true state of the system to enhance the control requirement and automation of the microgrid. To achieve the true state of a microgrid, this study proposes the use of an algorithm based on the unscented kalman filter (UKF). The proposed state estimator technique is developed using an unscented-transformation and sigma-points measurement technique capable of minimizing the mean and covariance of a nonlinear cost function to estimate the true state of a single-phase, three-phase single-source and three-phase multi-source microgrid system. The advantage of the proposed estimator over using extended kalman filter (EKF) is investigated in simulations. The results demonstrate that the use of the UKF estimator produces a superior estimation of the system compared with the use of the EKF. An adaptive PID controller is also developed and used in system conjunction with the estimator to regulate its voltage and current against the number of loads. Deviation in load parameters hamper the function of the MG system. The performance of the developed controller is also evaluated against number of loads. Results indicate the controller provides a more stable and high-tracking performance with the inclusion of the UKF in the system.

关键词： Microgrids Voltage control Packet loss Kalman filters Current control Adaptation models Microgrid model reference adaptive control state estimation unscented kalman filter voltage and current control

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3 Robust flexible adaptation gain based CRM for guaranteed transient performance

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2019年第5期41卷 1233-1242页

作者： Dey, Rajiv Jain, Sachin K. Padhy, Prabin K. IIITDM Jabalpur India

High adaptation gain in model reference adaptive control (MRAC) with closed-loop reference models (CRMs) is a necessary requirement to ensure guaranteed transient performance with better tracking and fast convergence. The high adaptation gain, however, may excite unmodelled dynamics, leading to instability, making the differential equations of the adaptive law stiff and causing numerical instability. Therefore, it becomes apparent that a compromise is required on either convergence speed and transient performance or system stability. This paper attempts to address these issues with a novel CRM-MRAC architecture with flexible adaptation gain, which varies as a function of the derived parameter estimation error. The proposed adaptive scheme mathematically ensures a designable upper bound of the L infinity norm;it also ensures the absence of high-frequency oscillations in the control input, thereby making the system more robust and stable. The effectiveness of the proposed work has been validated with simulation studies on a standard numerical example and the performance has been compared with recent similar works.

关键词： adaptive control model reference adaptive control linear uncertain dynamical system fast adaptation

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