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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE controllers Under External Uncertainties

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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Development and Optimization of a WebGL-Based Mechanical Model Simulation Platform 4th

Development and Optimization of a WebGL-Based Mechanical Mod...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wang, Xintong Tian, Hao Fang, Jingzhe Zhang, Hualiang Zhang, Tao Software College Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789819778867

The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation platforms are severely limited by the issue of sluggish rendering performance, which frequently arises when obtaining high-quality rendering. When drawing high demand mechanical models, a lot of processing power is frequently needed, which makes real-time rendering speed unsatisfactory for practical purposes. In order to achieve real-time rendering and simulation of 3D robots, this experiment built and implemented a WebGL-based 3D simulation platform. Additionally, the simulation platform can be optimized by increasing frame rate and rendering speed without sacrificing rendering effect by building a mixed bounding box. © The Author(s) 2025.

关键词： Digital elevation model

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Electric Vehicle Routing Problem with Capacitated Charging Stations and Dynamic Electricity Prices

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IEEE Internet of Things Journal 2025年

作者： He, Zhiye Tian, Yuning Miao, Haoyu Yao, Canqi Yang, Zaiyue Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities School of System Design and Intelligent Manufacturing Shenzhen518055 China The Hong Kong Polytechnic University Department of Logistics and Maritime Studies Hong Kong Hong Kong

With rapid electrification of transportation systems in recent years, the electric vehicle routing problem (EVRP) has received increasing attention. In order to serve a set of valuable transportation tasks using a fleet of electric vehicles, EVRP intends to jointly determine their routes and the charging schedules, with the aim of optimizing service reward and charging cost under state of charge (SOC) constraints of battery. EVRP is a difficult nonlinear integer program, and due to its intrinsic NP-hardness early EVRP studies often oversimplify the charging process and assume unlimited capacity of charging stations and constant electricity price. The contribution of this paper lies in twofold. First, to better characterize the practical charging process, this paper introduces a new graph model that can take the charging station capacity and dynamic electricity price into full consideration. Second, we design a series of transformation techniques, including reformulation SOC dynamics, Lagrangian relaxation, exact linear programming relaxation, etc, which eventually convert the original EVRP into an approximate linear program. Extensive simulations across 14 instances demonstrate that, the proposed approach is capable of solving large-scale instances over 500 nodes, and is 100 times faster than traditional analytic methods while achieving very close optimality. While comparing with the popular heuristic method ALNS (Adaptive Large Neighborhood Search), our method is several times faster and obtains much better optimality. © 2014 IEEE.

关键词： Charging stations

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and Engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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Deep reinforcement learning-based trajectory planning for double pendulum cranes: Design and experiments

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Mechanical systems and Signal Processing 2025年 234卷

作者： Ding, Weili Zhang, Heng Hua, Changchun Lu, Biao Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Since cranes usually possess double pendulum dynamics, the mass of the payload often changes and there are frequently lifting/lowering operations simultaneously. Moreover, most crane systems are driven by motors, whose velocities and accelerations are often limited. To solve the above problems, this paper proposes a deep reinforcement learning (DRL) reference trajectory generation method based on virtual–physical joint training. Firstly, a DRL module based on deep deterministic policy gradient (DDPG), along with a double pendulum crane dynamic model and an adaptive controller, are established within the virtual environment for training the reference trajectory. When the reward reaches the threshold, the training is switched to the physical environment to further optimize the reference trajectory, realizing the swing suppression of the hook and the payload. In addition, to satisfy the performance of the drive motors, velocity and acceleration thresholds are set to constrain the performance of the drive motors. Finally, in view of the fact that the operation process of the double pendulum crane is divided into payload transportation and payload loading/unloading, an event-triggering mechanism is designed to switch different control policies in accordance with different operation processes, thus reducing the consumption of computing resources. Through experiments on the actual double pendulum crane and comparison with the existing reference trajectories and input shapers, the superiority of this method is demonstrated. © 2025 Elsevier Ltd

关键词： Model reference adaptive control

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Obstacle-Avoidance Motion Planning Method of Manipulator Based on Deep Reinforcement Learning

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Unmanned systems 2025年

作者： Liu, Yiyang Wu, Defan Fu, Yukai Bai, Hongfei Deng, Chao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

In recent years, driven by the rapid advancement of industrial automation and intelligent manufacturing, the demand for manipulators to perform tasks in complex environments has significantly increased. Traditional motion planning methods face challenges of computational complexity and lack of adaptability when handling robots with redundant degrees of freedom. To address these issues, this paper proposes a novel obstacle avoidance motion planning method for manipulators based on deep reinforcement learning (DRL), which is especially important when unmanned systems operate in a GPS-free environment. The primary contributions are twofold: (i) we propose an innovative algorithm, termed PPO-HER, which incorporates hindsight experience replay (HER) into proximal policy optimization (PPO) during training, effectively leveraging failed experiences to enhance policy learning efficiency;(ii) an adaptive action exploration strategy is designed to accelerate training convergence and avoid local optima. Experimental results demonstrate that the PPO-HER algorithm and the adaptive action exploration strategy perform exceptionally well across three environments of varying difficulty, significantly improving network training speed and success rates, shortening planned paths, and enhancing autonomous obstacle avoidance capabilities. These findings provide new ideas for target location and tracking of unmanned systems, and lay the foundation for future learning and data-driven approaches in complex environments. © World Scientific Publishing Company.

关键词： Deep reinforcement learning

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

arXiv

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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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