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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Kiruba Sandou D Sunad kumara A N Binod Kumar Choudhary Shashikala Gurpur Sarishma Natrayan L M. SivaramKumar Department of Robotics and Automation Manakula Vinayagar Institute of Technology Pondicherry India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Electrical and Electronics Engineering ARKA Jain University Jamshedpur Jharkhand India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of EEE Karpagam Academy of higher education Coimbatore Tamiinadu

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications.

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Relative Error-based Time-limited H2 Model Order Reduction via Oblique Projection

arXiv

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arXiv 2022年

作者： Zulfiqar, Umair Du, Xin Song, Qiuyan Xiao, Zhi-Hua Sreeram, Victor School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai200444 China School of Information and Mathematics Yangtze University Hubei Jingzhou434023 China Department of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia

In time-limited model order reduction, a reduced-order approximation of the original high-order model is obtained that accurately approximates the original model within the desired limited time interval. Accuracy outside that time interval is not that important. The error incurred when a reduced-order model is used as a surrogate for the original model can be quantified in absolute or relative terms to access the performance of the model reduction algorithm. The relative error is generally more meaningful than an absolute error because if the original and reduced systems’ responses are of small magnitude, the absolute error is small in magnitude as well. However, this does not necessarily mean that the reduced model is accurate. The relative error in such scenarios is useful and meaningful as it quantifies percentage error irrespective of the magnitude of the system’s response. In this paper, the necessary conditions for a local optimum of the time-limited H2 norm of the relative error system are derived. Inspired by these conditions, an oblique projection algorithm is proposed that ensures small H2-norm relative error within the desired time interval. Unlike the existing relative error-based model reduction algorithms, the proposed algorithm does not require solutions of large-scale Lyapunov and Riccati equations. The proposed algorithm is compared with time-limited balanced truncation, time-limited balanced stochastic truncation, and time-limited iterative Rational Krylov algorithm. Numerical results confirm the superiority of the proposed algorithm over these existing algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Riccati equations

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea*

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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IEEE International Conference on robotics and Biomimetics

作者： Yanyan Zhang Menglong Ding Shiqiang Zhu Yiming Liang Yunhe Bai Keren Xie Minhong Wan Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Hangzhou China

ISBN: (纸本)9781665481106

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration.

关键词： Biological system modeling Kinematics Soft robotics Propulsion Fish Dielectric elastomers Numerical models

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Frequency-limited H2 Model Order Reduction Based on Relative Error

arXiv

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arXiv 2022年

Frequency-limited model order reduction aims to approximate a high-order model with a reduced-order model that maintains high fidelity within a specific frequency range. Beyond this range, a decrease in accuracy is acceptable due to the nature of the problem. The quality of the reduced-order model is typically evaluated using absolute or relative measures of approximation error. Relative error, which represents the percentage error, becomes particularly relevant when reducing a plant model for the purpose of designing a reduced-order controller. This paper derives the necessary conditions for achieving a local optimum of the frequency-limited H2 norm for the relative error system. Based on these optimality conditions, an oblique projection algorithm is proposed to ensure a small relative error within the desired frequency interval. Unlike existing algorithms, the proposed approach does not necessitate solving large-scale Lyapunov and Ricatti equations. Instead, the proposed algorithm relies on solving sparse-dense Sylvester equations, which typically emerge in the majority of H2 model order reduction algorithms, but can be efficiently solved. To evaluate the performance of the proposed algorithm, a comparison is conducted with three existing techniques: frequency-limited balanced truncation, frequency-limited balanced stochastic truncation, and frequency-limited iterative Rational Krylov algorithm. The comparative analysis focuses on designing reduced-order controllers for high-order plants. Numerical results confirm that the reduced-order controllers obtained using the proposed algorithm ensure superior robust closed-loop stability. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Adaptive Control with Global Exponential Stability for Parameter-Varying Nonlinear Systems under Unknown Control Gains

arXiv

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arXiv 2022年

作者： Ye, Hefu Wu, Haijia Zhao, Kai Song, Yongduan Chongqing Key Laboratory of Autonomous Systems Institute of Artificial Intelligence School of Automation Chongqing University 400044 China Army Logistics Academy Chongqing401331 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this paper, without the need for PE condition, we address the problem of global exponential stabilization of strict-feedback systems with mismatched uncertainties and unknown yet time-varying control gains. The resultant control, embedded with time-varying feedback gains, is capable of ensuring global exponential stability of parametric-strict-feedback systems in the absence of persistence of excitation. By using the enhanced Nussbaum function, the previous results are extended to more general nonlinear systems where the sign and magnitude of the time-varying control gain are unknown. In particular, the argument of the Nussbaum function is guaranteed to be always positive with the aid of nonlinear damping design, which is critical to perform a straightforward technical analysis of the boundedness of the Nussbaum function. Finally, the global exponential stability of parameter-varying strict-feedback systems, the boundedness of the control input and the update rate, and the asymptotic constancy of the parameter estimate are established. Numerical simulations are carried out to verify the effectiveness and benefits of the proposed methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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IEEE International Conference on robotics and Biomimetics

作者： Yanyan Zhang Yiming Liang Shiqiang Zhu Menglong Ding Yunhe Bai Haoyuan Du Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou China

ISBN: (纸本)9781665481106

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses.

关键词： Pain Friction Biological system modeling Lubrication Surface texture Optimization Surface treatment

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration

arXiv

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arXiv 2022年

作者： Chen, Tan Huang, Zhe Motes, James Geng, Junyi Ta, Quang Minh Dinkel, Holly Abdul-Rashid, Hameed Myers, Jessica Mun, Ye-Ji Lin, Wei-Che Huang, Yuan-Yung Liu, Sizhe Morales, Marco Amato, Nancy M. Driggs-Campbell, Katherine Bretl, Timothy The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Department of Aerospace Engineering The University of Illinois Urbana-Champaign United States Foxconn Interconnect Technology Taiwan

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies. © 2022, CC BY.

关键词： Collaborative robots

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