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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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Integrated Cross-Scale Manipulation and Modulable Encapsulation of Cell-Laden Hydrogel for Constructing Tissue-Mimicking Microstructures

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Research 2025年第1期2024卷 704-717页

作者： Yanfeng Zhao Xinyi Dong Yang Li Juan Cui Qing Shi Hen-Wei Huang Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Peking University First Hospital Xicheng DistrictBeijing 100034China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationNorth University of ChinaTaiyuan 030051China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Laboratory for Translational Engineering Harvard Medical SchoolCambridgeMA 02139USA Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of EducationBeijing 100081China

Engineered microstructures that mimic in vivo tissues have demonstrated great potential for applications in regenerative medicine,drug screening,and cell behavior ***,current methods for engineering microstructures that mimic the multi-extracellular matrix and multicellular features of natural tissues to realize tissue-mimicking microstructures in vitro remain ***,we propose a versatile method for constructing tissue-mimicking heterogeneous microstructures by orderly integration of macroscopic hydrogel exchange,microscopic cell manipulation,and encapsulation ***,various cell-laden hydrogel droplets are manipulated at the millimeter scale using electrowetting on dielectric to achieve efficient hydrogel ***,the cells are manipulated at the micrometer scale using dielectrophoresis to adjust their density and arrangement within the hydrogel ***,the photopolymerization of these hydrogel droplets is triggered in designated regions by dynamically modulating the shape and position of the excitation ultraviolet ***,heterogeneous microstructures with different extracellular matrix geometries and components were constructed,including specific cell densities and *** resulting heterogeneous microstructure supported long-term culture of hepatocytes and fibroblasts with high cell viability(over 90%).Moreover,the density and distribution of the 2 cell types had significant effects on the cell proliferation and urea *** propose that our method can lead to the construction of additional biomimetic heterogeneous microstructures with unprecedented potential for use in future tissue engineering applications.

关键词： cell behavior explorationhowevercurrent tissue mimicking microstructures cell laden hydrogel electrowetting dielectric regenerative medicinedrug screeningand heterogeneous microstructures engineered microstructures orderly integration

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A Machine Learning-Driven Model for Predicting the Productivity of Internet of Mechanical Things 2nd

A Machine Learning-Driven Model for Predicting the Productiv...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Sedik, Ahmed Nasralla, Moustafa M. Esmail, Maged Abdullah Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

ISBN: (纸本)9789819783472

This study delves into the utilization of artificial intelligence (AI) for predicting the performance of Hemispherical Solar Stills (HSS), aiming to transcend reliance on empirical methods. Employing real-world experimental data, five distinct prediction models were developed. The investigation revealed that the DT model emerged as the most efficacious in estimating both hourly productivity and instantaneous efficiency, showcasing remarkable accuracy and efficiency in its predictions. Statistical metrics demonstrated that the DT model's estimates for HSS productivity and efficiency were closely aligned with unity. This research accentuates the transformative potential of AI models in streamlining and enhancing predictive techniques for estimating HSS performance. The findings advocate for the adoption of AI-driven approaches as cost-effective and precise solutions for researchers and practitioners within the realm of mechanical engineering. By substantiating the efficacy of the DT model, this study contributes valuable insights that not only advance our understanding of AI applications in solar technology but also offer practical tools for optimizing the design and performance assessment of Hemispherical Solar Stills. The integration of AI in this context promises to usher in a new era of efficiency and accuracy, addressing challenges in the field and fostering advancements in sustainable energy technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Machine Learning-based Optimal Trajectory Planning for Spacecraft Passively Flying Around a Satellite for Proximity Operations

Machine Learning-based Optimal Trajectory Planning for Space...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Barth, Andrew Roach, Dylan Ma, Ou Hernandez, Michael Ashbaugh, Ryan Fleming, Alex Department of Aerospace Engineering Intelligent Robotics and Autonomous Systems Laboratory University of Cincinnati CincinnatiOH45221 United States iMETALX Inc SausalitoCA94965 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In-space or On-orbit Servicing, Assembly, and Manufacturing (ISAM or OSAM) activities require spacecraft to fly around a client satellite or another orbiting object in a proximity range from 100’s of meters to 0 meters in distance. When performing such proximity operations, it becomes extremely important for the servicing craft to not only avoid collision but at the same time fly close enough for better situational awareness and minimal fuel consumption. This is especially important because such orbiting spacecraft will often be low-cost CubeSats performing OSAM tasks, therefore, planning a Passive Relative Orbit (PRO) becomes essential for low-risk and energy-efficient operations. Next, the problem of how to deploy a CubeSat from a station-keeping Chief spacecraft along a transfer trajectory, followed by a PRO-based two-impulse burn maneuver with minimal fuel consumption and variable time constraints, needs to be solved. Once the task is completed, another two-burn transfer trajectory will be calculated for the servicing deputy to return to the Chief. In this paper, we use a machine learning approach to determine the ΔV burns required to achieve the relative transfer and task trajectories. Machine learning allows us to improve our mission planning capabilities and gives the system more freedom to make choices where the operator does not have clear or full insight of the complex physics under many constraints. The system was trained using the proximal policy optimization algorithm for a range of task trajectory parameters randomly selected for each training episode. The results of the trained system were validated by comparison to those produced by traditional optimization methods from our previous work. The robustness of the trained algorithm was verified by 1,000 random scenarios falling within the range of trained conditions. The study demonstrates the ability to use the trained model for a variety of situations without the need to retrain specially for each type

关键词： Orbits

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Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and Control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Mechanical Properties Inside Origami-Inspired Structures: An Overview

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Applied Mechanics Reviews 2025年第1期77卷 011001页

作者： Yan, Peng Huang, Hailin Meloni, Marco Li, Bing Cai, Jianguo Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China Harbin Institute of Technology Shenzhen518052 China Key Laboratory of C & PC Structures of Ministry of Education National Prestress Engineering Research Center Southeast University Nanjing211189 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Habin Institute of Technology Shenzhen518052 China

In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Degrees of freedom (mechanics)

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Robust Practical Stabilization for Complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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Aerodynamic Proximity Effect Predictions for Unmanned Aerial Rotorcraft

Aerodynamic Proximity Effect Predictions for Unmanned Aerial...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Baniecki, Annalise Inoyama, Daisaku Stoumbos, Tom Garrard, Yizhuang Zhang, Wenlong Robotics and Intelligent Systems Laboratory School of Manufacturing Systems and Networks Arizona State University MesaAZ85212 United States College of Engineering Purdue University West LafayetteIN47907 United States Northrop Grumman Systems Corporation Space Systems DullesVA20166 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

As unmanned aerial vehicles are seeing increased use close to surfaces, being able to predict and counteract the forces and torques induced by this proximity, or aerodynamic proximity effect, is necessary to maintain accurate trajectory tracking performance. However, it is difficult to predict the aerodynamic proximity effect, especially when there are corners where two or more surfaces intersect. In this work, a data-driven neural network approach to predict the aerodynamic proximity effect on a hexarotor is presented. Unlike most prior studies that focused primarily on ground effects, the work also considers the impact of one or more walls. Data is collected using a hexarotor mounted on a force/torque sensor across various orientations and proximities to surfaces. These measurements are used to train a feedforward deep neural network that predicts the aerodynamic proximity effect as a function of the UAV's pose relative to the nearby surfaces. The neural network can accurately predict the force and torque acting on the hexarotor Z axis, but fails to predict the X and Y force and torque components with the same level of accuracy. © 2024 Northrop Grumman systems Corporation

关键词： Unmanned aerial vehicles (UAV)

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Design and Analysis of a Compact and Foldable Master Device Based on Binocular Near-Infrared Optical Navigation Technology for Minimally Invasive Surgery Robots

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 514-527页

作者： Pan, Lizhi Jiang, Xu Ma, Zhikang Guan, Bo Yi, Bo Zhao, Jianchang Tianjin University Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Institute of Medical Robotics and Intelligent Systems Tianjin300350 China The Third Xiangya Hospital of Central South University Disease Professional Committee of Chinese Medical Doctor Association Surgeon Branch of Hunan Medical Doctor Association Hunan Provincial Key Laboratory of Surgical Robotics Hunan Changsha410013 China Tsinghua University School of Aeronautics and Astronautics Beijing100084 China

The traditional minimally invasive surgical (MIS) robots generally have bulk leader manipulators with relatively fixed working positions, which limits their further utilization in special scenarios, such as remote surgeries. This study proposes a compact and foldable leader device based on passive binocular near-infrared (NIR) optical navigation technology for MIS robots, which does not need mechanical arm linkage constraints and provides a larger range of position and orientation tracking, enabling the surgeons to perform continuous leader-follower manipulations more steadily. Moreover, the polyhedral and foldable structure of the optical leader device further reduces the spatial footprint of the MIS robot. A prototype of the optical leader devices was constructed with a weight of 186 g. Its performance was then evaluated through testing, and the maximum average absolute error in position and orientation tracking was 0.90 mm and 0.45°, respectively. Additionally, the prototype exhibits acceptable stability and a wide range of position and orientation tracking. The leader device features a compact, foldable structure with enhanced portability and excellent position and orientation tracking capabilities, facilitating precise surgical maneuvers of surgeons in scenarios of remote surgeries. © 2018 IEEE.

关键词： Knee prostheses

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