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Resilient human-in-the-loop Formation-Tracking of Multi-UAV Systems Against Byzantine Attacks

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年 22卷 3797-3809页

作者： Gong, Xin Gui, Jie Chen, Yong Yang, Xuefei Yu, Wenwu Huang, Tingwen Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Cent South Univ Sch Automat Changsha 410000 Peoples R China Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 211189 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This study addresses resilient human-in-the-loop (HiTL) formation-tracking of multi-UAV systems against f-local Byzantine attacks. In the HiTL settings, a human operator plays a key role in detecting any physical hazard, monitoring the whole UAV swarm, and sending secure execution signals to a non-autonomous leader UAV. Moreover, there exists a fraction of Byzantine UAVs in the multi-UAV systems, which propagate incorrect information to their neighbors (called Byzantine edge attacks (BEAs)) and adopt false input signals (called Byzantine node attacks (BNAs)) when swarming. In order to suppress the above aggressive Byzantine attacks, this paper proposes a Byzantine-resilient hierarchical control scheme, including a virtual Digital Twin Layer (DTL) apart from a Cyber-Physical Layer (CPL). First, a distributed resilient estimation scheme is proposed on the DTL, which can realize resilient estimation on the state of the non-autonomous leader UAV against BEAs on the premise that the DTL topology is strongly (2f+1)-robust. Second, a series of decentralized and chattering-free controllers is formulated on the CPL, which is resilient to both BNAs and inter-layered faults. The asymptotical control performance of the above controllers is strictly proven based on Cromwell-Bellman Lemma. To demonstrate the practicality of the theoretical results, a resilient HiTL multi-UAV systems experiment has been further conducted. The experimental results verify the effectiveness and practicality of the designed two-layered controllers against f-local Byzantine attacks. Note to Practitioners-Owing to the wide application of multi-UAV systems, the resilience of the whole swarm against malicious attacks has grasped the great attention of both academia and industry. This work considers a rather aggressive kind of attacks, named Byzantine attacks, where a fraction of unidentified UAVs act as traitors. Inspired by the digital twin technology, a two-layered control architecture for multi-UAV sys

关键词： Autonomous aerial vehicles Estimation human in the loop Heuristic algorithms Digital twins Protocols Directed graphs Byzantine attacks human-in-the-loop control multi-UAV systems resilient formation-tracking

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Flexible Quadrotor Unmanned Aerial Vehicles: Spatially Distributed Modeling and Delay-Resistant control

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JOURNAL OF GUIDANCE control AND DYNAMICS 2024年第6期47卷 1109-1122页

作者： Eraslan, Emre Yildiz, Yildiray Univ Illinois Champaign IL 61820 USA Bilkent Univ TR-06800 Ankara Turkiye

This paper introduces an analytical framework for the derivation of distributed-parameter equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, to facilitate the controller design. A delay-resistant low-frequency adaptive controller is employed, which prevents excessive oscillations due to flexible dynamics, compensates uncertainties, and addresses the inherent time delay. In addition to this, a delay-dependent stability condition for the overall system dynamics is obtained including the human operator with reaction time delay, the adaptive controller, and the flexible quadrotor dynamics with input delay. With comparative simulation studies, it is demonstrated that the flexible arm tip oscillations are significantly reduced when the low-frequency delay-resistant closed-loop reference model adaptive controller is used, compared to a closed-loop reference model adaptive controller and a conventional model reference adaptive controller.

关键词： Model Reference Adaptive control Quadcopter Unmanned Aerial Vehicle Military Technology Mechanical and Structural Vibrations Aerospace Engineering Aircraft Flight control System Flexible Dynamics Closed-loop Reference Model Adaptive control human-in-the-loop control

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Revisiting Game-Theoretic control in Socio-Technical Networks: Emerging Design Frameworks and Contemporary Applications

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IEEE control SYSTEMS LETTERS 2025年 9卷 74-89页

作者： Zhu, Quanyan Basar, Tamer NYU Dept Elect & Comp Engn New York NY 11201 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

Socio-technical networks represent emerging cyber-physical infrastructures that are tightly interwoven with human networks. The coupling between human and technical networks presents significant challenges in managing, controlling, and securing these complex, interdependent systems. This letter investigates game-theoretic frameworks for the design and control of socio-technical networks, with a focus on critical applications such as misinformation management, infrastructure optimization, and resilience in socio-cyber-physical systems (SCPS). Core methodologies, including Stackelberg games, mechanism design, and dynamic game theory, are examined as powerful tools for modeling interactions in hierarchical, multi-agent environments. Key challenges addressed include mitigating human-driven vulnerabilities, managing large-scale system dynamics, and countering adversarial threats. By bridging individual agent behaviors with overarching system goals, this letter illustrates how the integration of game theory and control theory can lead to robust, resilient, and adaptive socio-technical networks. This letter highlights the potential of these frameworks to dynamically align decentralized agent actions with system-wide objectives of stability, security, and efficiency.

关键词： Game theory Resilience Stability analysis Games control theory Sociotechnical systems Shape Behavioral sciences Security Process control control of networks human-in-the-loop control control applications agents-based systems

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Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine

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IEEE control SYSTEMS LETTERS 2024年 8卷 3081-3086页

作者： Shao, Ketong Chakrabarty, Ankush Mesbah, Ali Romeres, Diego Univ Calif Berkeley Dept Chem & Biomol Engn Berkeley CA 94720 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

The design of advanced learning- and optimization-based controllers requires selecting parameters that balance performance objectives and constraints. Bayesian optimization (BO) has proven effective for resource-efficient calibration of such controllers. Preference-guided BO incorporates user preferences to prioritize areas of interest, but it lacks a mechanism for users to specify desired outcomes directly. This letter introduces a user-centric framework for preference-guided BO, leveraging a novel knowledge-gradient based coactive acquisition function that allows users not only to select preferred outcomes but also also propose alternatives to guide exploration. To enable efficient implementation, we approximate the acquisition function, avoiding costly bilevel optimization. The approach is validated for control policy adaptation in personalized plasma medicine, where it outperforms standard preference-guided BO by effectively integrating user feedback to personalize treatment protocol.

关键词： Optimization Bayes methods Computational modeling Plasmas Noise measurement Calibration Vectors Predictive models Kernel Gaussian processes Biomedical optimization human-in-the-loop control

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Data-Driven Architecture to Encode Information in the Kinematics of Robots and Artificial Avatars

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IEEE control SYSTEMS LETTERS 2024年 8卷 1919-1924页

作者： De Lellis, Francesco Coraggio, Marco Foster, Nathan C. Villa, Riccardo Becchio, Cristina di Bernardo, Mario Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Scuola Super Meridionale Modeling & Engn Risk & Complex I-80138 Naples Italy Univ Med Ctr Hamburg Eppendorf Dept Neurol D-20251 Hamburg Germany

We present a data-driven control architecture designed to encode specific information, such as the presence or absence of an emotion, in the movements of an avatar or robot driven by a human operator. Our strategy leverages a set of human-recorded examples as the core for generating information-rich kinematic signals. To ensure successful object grasping, we propose a deep reinforcement learning strategy. We validate our approach using an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.

关键词： Encoding Kinematics Avatars Task analysis Reinforcement learning Redundancy Real-time systems Data-driven control machine learning human-in-the-loop control

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Integrating Inverse Reinforcement Learning and Direct Policy Search for Modeling Multipurpose Water Reservoir Systems

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IEEE control SYSTEMS LETTERS 2024年 8卷 1529-1534页

作者： Giuliani, Matteo Castelletti, Andrea Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy

System identification and optimal control have always contributed to water resources systems planning and management. Although water control problems are commonly formulated as multi-objective Markov Decision Processes, accurately modeling reservoir systems controlled by human operators remains challenging due to the absence of a formal definition of the objective function guiding their behavior. In this letter, we introduce a mixed Reinforcement Learning approach to model the dynamics of multipurpose reservoir systems. Specifically, our method first uses Inverse Reinforcement Learning to extract the tradeoff among competing objectives from historical observations of the reservoir system dynamics. The identified objective function is then used in the formulation of an optimal control problem returning a closed-loop policy which allows the simulation of the observed dynamics of the reservoir system. We demonstrate the potential of the proposed method in a real-world application involving the multipurpose regulation of Lake Como in northern Italy. Results show that our approach effectively infers the tradeoff between flood control and water supply adopted in the observed system's operation, and yields a control policy that closely approximates the observed system dynamics.

关键词： Reservoirs Jacobian matrices Optimal control Lakes Reinforcement learning Vectors Linear programming Inverse reinforcement learning direct policy search machine learning human-in-the-loop control control applications

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A semi-autonomous robot control based on bone layer transition detection for a safe pedicle tapping

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2023年第10期18卷 1745-1755页

作者： Lauretti, Clemente Cordella, Francesca Saltarelli, Ilenia Morfino, Rosaura Zollo, Loredana Univ Campus Biomed Unit Adv Robot & Human Centred Technol CREO lab Via Alvaro Portillo 21 I-00128 Rome Italy

Purpose Automatic robotic platforms for robot-aided spinal surgery are mostly employed for drilling the pedicle screw path and do not adapt the tool rotational speed depending on the variation of the bone density. This feature is highly desirable in control strategies for robot-aided pedicle tapping, which may result in a poor quality thread if the surgical tool speed is not adequately tuned depending on the bone density to be threaded. Therefore, the objective of this paper is to propose a novel semi-autonomous control for robot-aided pedicle tapping that is able to (i) identify the bone layer transition, (ii) adapt the tool velocity depending on the detected bone layer density and (iii) stop the tool tip before propulsion of the bone *** The proposed semi-autonomous control for pedicle tapping consists of: (i) a hybrid position/force control loop that allows the surgeon to move the surgical tool along a pre-planned axis and (ii) a velocity control loop that allows him/her to finely tune the tool rotational speed by modulating the tool-bone interaction force along the same axis. The velocity control loop integrates also a bone layer transition detection algorithm that dynamically limits the tool velocity depending on the bone layer density. The approach was tested on the Kuka LWR4+ provided with an actuated surgical tapper which was used to tap a wood specimen simulating the bone layer density characteristics and bovine *** A normalized maximum time delay in the bone layer transition detection of 0.25 was achieved by the experiments. A success rate of 100% was achieved for all the tested tool velocities. The proposed control achieved a maximum steady-state error of 0.4 *** The study demonstrated high capability of the proposed approach to i) promptly detect transition among the specimen layers and ii) adapt the tool velocities depending on the detected layers.

关键词： Robotic surgery Spinal surgery Tapping procedure human-in-the-loop control

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Nonlinear MPC for Insulin Titratio2shn of Type 2 Diabetes 8

Nonlinear MPC for Insulin Titratio2shn of Type 2 Diabetes

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8th IFAC Conference on Nonlinear Model Predictive control (NMPC)

作者： Midtgaard, Joachim Melkaer Bruhn, Kasper Jorgensen, Michael Siggaard Johnasen, Tristan Grusgaard Gronkjaer, Kasper Kjaerulff Ahmed, Faiyaz Alvi Al Ahdab, Mohamad Leth, John Aalborg Univ Dept Elect Syst Sect Control & Automat Fredrik Bajers Vej 7C DK-9220 Aalborg Denmark

This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D. Copyright (C) 2024 The Authors.

关键词： Nonlinear MPC Biological systems Diabetes human-in-the-loop control

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Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2022年第2期30卷 654-666页

作者： Corno, Matteo Duz, Alessandra Savaresi, Sergio M. Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

This article proposes an energy management system (EMS) for shared electric bicycles. The objective is to guarantee electric assistance to the cyclist while avoiding discharging the battery. The basic working principle exploits the cycling efficiency gaps. The proposed multilayered EMS is specifically tailored to a free-floating bike-sharing setting. The innermost layer manages the assistance and energy harvesting with the objective of yielding an intuitive human-machine interface. The middle level modulates the level of assistance so to track a desired average battery power. This is an adaptive model-based controller designed on a control-oriented model of the cyclist and bicycle energy dynamics. A cyclist profiling mechanism enables the model adaptation. The outermost loop guarantees the long-term robustness by tracking a desired battery state-of-charge profile. Extensive simulations and experimental tests validate this approach in terms of usability and charge sustenance, proving that the cyclist profiling is of paramount importance.

关键词： Bicycles Batteries Energy management Traction motors Wheels Vehicle dynamics Brushless DC motors Electrically assisted bicycles energy optimization hybrid vehicles human-in-the-loop control intelligent transportation systems

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Towards a more efficient human-exoskeleton assistance

Towards a more efficient human-exoskeleton assistance

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Monteiro, Sara Figueiredo, Joana Santos, Cristina Univ Minho LABBELS Associat Lab Ctr MicroElectroMech Syst Braga Portugal

ISBN: (纸本)9798350301212

There is evidence that the energy expended by humans can be reduced by wearing lower limb exoskeletons with user-oriented assistance strategies, such as human-in-theloop (HITL) controllers. HITL algorithms can be implemented in exoskeletons for the automatic and online optimization of controller parameters, such as the torque profile, depending on the energy expenditure (EE) measured in real-time. This way, it is possible to minimize the EE and tailor the exoskeleton assistance for each specific user. But measuring EE is not trivial. It is more commonly estimated by indirect calorimetry, however, this method requires expensive equipment, takes too long, and is infeasible for everyday use in the real world. Therefore, this study explores machine and deep learning regression models (RMs) as EE estimators in different motor activities based on data acquired by wearable sensors and anthropometric features. Several inputs were tested but the best performance was achieved by the heart rate, the 3-axis acceleration of the chest, wrist, thigh, and ankle, and the body mass index. Results from a public dataset are presented, after the preprocessing of the data. The bestperforming RM was an exponential Gaussian process regressor (GPR), that obtained root-mean-squared errors of 0.56 W/kg, 0.45 W/kg, and 0.60 W/kg for the standing, sitting, and walking activities, respectively. The GPR model outperformed a support vector machine, a boosted decision tree, a bagged decision tree, and a convolutional neural network.

关键词： artificial intelligence energy expenditure human-in-the-loop control exoskeleton assistance wearable sensors

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