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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy systematics Wheelchairs Estimation Predictive models Real-time systems

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on robotics and automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models Control systems Data models Nonlinear dynamical systems

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Small Sample Learning Mechanism Based on Knowledge-transferring for Fault Diagnosis

Small Sample Learning Mechanism Based on Knowledge-transferr...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xue Ma Xinyu Wu Chenglin Wen Xiaohui Sun Department of Automation Tsinghua University Beijing China Shenzhen Institutes of Advanced Technology Guangdong Provincial Key Lab of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Department of Automation Guangdong University of Petrochemical Technology Maoming China

Deep learning-based methods have been applied in many fields which has received increased attention. It requires large annotated data to train a deep model with good generalization ability. However, it may be very difficult, time-consuming, and even expensive to collect and annotate practical industrial data, for example, for fault diagnosis in industrial systems. To address this issue, a novel small sample learning (SSL) mechanism based on Knowledge-transferring is proposed in this manuscript, which leverages the expert system and deep neural network simultaneously. Firstly, an annotator is constructed based on a belief rule base (BRB) expert system only with available small labeled samples. Then, the left unlabeled samples will be labeled through the annotator to form an augmented training dataset (ATD) with the available small labeled samples, which can realize the data representation of expert knowledge. Besides, a united optimization model based on CNN that combines data and knowledge is built, which has realized the transfer of expert knowledge. Finally, we demonstrate the capability of the proposed method on the dataset collected from the real experimental platform for rotating machinery fault diagnosis.

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BCLH2Pro:A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

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Energy and AI 2024年第4期18卷 13-26页

作者： Thanadol Tuntiwongwat Sippawit Thammawiset Thongchai Rohitatisha Srinophakun Chawalit Ngamcharussrivichai Somboon Sukpancharoen Department of Mechanical Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Automation Robotics and Intelligent System Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Chemical Engineering Faculty of EngineeringKasetsart UniversityBangkok 10900Thailand Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals(CBRC) Faculty of ScienceChulalongkorn UniversityPathumwanBangkok 10330Thailand Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400SelangorMalaysia Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Center for Alternative Energy Research and Development Khon Kaen UniversityKhon Kaen 40002Thailand

This study optimizes biomass chemical looping processes(BCLpro),a technique for converting biomass to energy,through machine learning(ML)for sustainable energy *** study proposes an integrated Fe_(2)O_(3)-based,BCLpro combining steam gasification for H2 *** Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model.A methodology involving K-Nearest Neighbors(KNN),Extreme Gradient Boosting(XGB),Light Gradient Boosting Machine(LGBM),Support Vector Machine(SVM),Random Forest(RF),and CatBoost(CB)algorithms was employed to predict H_(2)yields in the BCLpro,utilizing 10-fold cross-validation for robust model *** highlight the CB algorithm’s superior performance,achieving up to 98%predictive accuracy,with carbon content,reducer temperature,and Fe_(2)O_(3)/Al_(2)O_(3)mass ratio identified as crucial *** algorithm has been developed into a user-friendly tool,BCLH2Pro,accessible via a web *** tool is designed to assist in reducing costs,optimizing biomass selection,and planning operational conditions to maximize H2 yield in BCLpro *** to the tool can be obtained through the following link:http://***/.

关键词： Artificial intelligence Supervised learning Process simulation Chemical looping Agricultural waste

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Step-by-step Linearized Kalman Filter-Based Fault Detection of Workpiece Machining

Step-by-step Linearized Kalman Filter-Based Fault Detection ...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xiaohui Sun Xinyu Wu Chenglin Wen Xue Ma Department of Automation Tsinghua University Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Automation Guangdong University of Petrochemical Technology Maoming China

This paper is concerned with step-by-step linearized Kalman filter to deal with multiplicative faults diagnosis problem for class of nonlinear systems. Multiplicative fault variables are defined as parameter variables to be estimated, and linear dynamic models between fault variables and all variables are established. The indirect linear state and measurement models of fault variables and the corresponding step-by-step linearized Kalman filters are established based on current estimated values of all variable and some obtained estimated values of future variables; further, combined with fault variables, a filter is designed for state variables in the linearized system. On basis of EKF, the redisual generation is obtained. The effectiveness of the novel filter is verified in workpiece machining process.

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

arXiv

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

关键词： Model predictive control

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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IEEE International Conference on robotics and Biomimetics

作者： Muyang Hou Zhenya Lei Haidong Hu Zhong Wang Yong Wang Le Xie Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Beijing Institute of Control Engineering Beijing China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant; on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A* which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion.

关键词： Hands Trajectory tracking Trajectory planning Service robots Stability analysis Safety Mobile robots Resource management Splines (mathematics) Trajectory optimization

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Design and preliminary testing of a piezoelectric-based sole energy harvester

Design and preliminary testing of a piezoelectric-based sole...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Yidi Zhao, Xuan Shi, Qiuyu Liao, Haisu Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong

ISBN: (纸本)9781665481090

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W. © 2022 IEEE.

关键词： Cams

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Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Lateral Walking Gait Phase Detection with Data Optimization ...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Wujing Cao Xinbo Zhu Rui Xu Changyu Li Hongliu Yu Mingming Zhang Haoyong Yu Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jining Zhongke Intelligent Technology Co. Ltd. University of Shanghai for Science and Technology Southern University of Science and Technology Department of Biomedical Engineering National University of Singapore. National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

关键词： Legged locomotion Training Support vector machines Solid modeling Machine learning algorithms Accuracy Exoskeletons

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