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arXiv 2021年

作者： Bobtsov, Alexey Nikolaev, Nikolay Ortega, Romeo Efimov, Denis Kozachek, Olga Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 Saint-Petersburg197101 Russia Departamento Académico de Sistemas Digitales ITAM Ciudad de México Mexico INRIA Univ. Lille CNRS UMR 9189 CRIStAL LilleF-59000 France Laboratory "Control of Complex Systems" Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg199178 Russia

In this paper we address the problem of adaptive state observation of affine-in-the-states time-varying systems with delayed measurements and unknown parameters. The development of the results proposed in the [Bobtsov et al. 2021a] and in the [Bobtsov et al. 2021c] is considered. The case with known parameters has been studied by many researchers-see [Sanz et al. 2019, Bobtsov et al. 2021b] and references therein-where, similarly to the approach adopted here, the system is treated as a linear time-varying system. We show that the parameter estimation-based observer (PEBO) design proposed in [Ortega et al. 2015, 2021] provides a very simple solution for the unknown parameter case. Moreover, when PEBO is combined with the dynamic regressor extension and mixing (DREM) estimation technique [Aranovskiy et al. 2016, Ortega et al. 2019], the estimated state converges in fixed-time with extremely weak excitation assumptions. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

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Finite-time Frequency Estimator for Harmonic Signal

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IFAC-PapersOnLine 2020年第2期53卷 584-589页

作者： Alexey A. Bobtsov Anastasiia O. Vediakova Nikolay A. Nikolaev Olga V. Slita Anton A. Pyrkin Alexey A. Vedyakov Department of Control Systems and Robotics ITMO University Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Applications and Systems Saint Petersburg State University Saint Petersburg Russia

This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

关键词： parameter estimation observers frequency estimation finite-time estimation

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A Two-Stage Learning Framework for Driver Lane Change Intention Inference

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IFAC-PapersOnLine 2020年第5期53卷 638-643页

作者： Yang Xing Bin Tian Chen Lv Dongpu Cao School of Mechanical and Aerospace Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100190 China Department of Mechanical and Mechatronics Engineering University of Waterloo ON N2L3G1 Canada

With the rapid development of intelligent vehicles, human drivers are sharing the control authority with the automation functionalities. The mutual understanding between the intelligent vehicle and the human driver is the key to the efficient multi-agent teaming and the collaborative driving system. In this study, a two-stage learning framework for spatial-temporal driver lane change intention inference is developed. The two-stage learning framework contains two separate parts. First, an operational level driving behavior recognition system based on the deep convolutional neural network (CNN) is developed to recognize the driver behaviors, including mirror checking and normal driving. Then, based on the driver behavior features from the CNN, the sequential feature vector will be used for the construction of driver intention inference model based recurrent neural networks (RNN) and long short-term memory (LSTM). The proposed model achieved a state-of-the-art result on the driver lane change intention inference task. Based on the naturalistic driving dataset, the model achieved over 91% accuracy for lane change and lane-keeping intention prediction. The two-stage learning framework can significantly increase the model flexibility and accuracy, which makes it easier to be implemented and updated in intelligent vehicles.

关键词： Driver Behavior Driver Model Deep Learning LSTM Intelligent Vehicle

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Neural Network Tracking of Switched Robotic Manipulators with Mode-Dependent Average Dwell Time

Neural Network Tracking of Switched Robotic Manipulators wit...

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

作者： Chao Ma Wei Wu Yidao Ji Wei Wang School of Mechanical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper is concerned with the tracking control for robotic manipulators with switching loads and parameter uncertainties. More precisely, the neural network approximation method is adopted with adaptive control to deal with the unmodeled dynamics. Multiple Lyapunov function method is used based on mode-dependent average dwell time, such that the tracking error can converge with desired region. A numerical example is finally provided for illustrating our proposed tracking strategy.

关键词： Switches Robots Manipulators Neural networks control systems Manipulator dynamics Uncertain systems

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Neural-based Inverse Kinematical Analysis for Bio-inspired Cable-driven Robotic Manipulator

Neural-based Inverse Kinematical Analysis for Bio-inspired C...

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

作者： Hang Fu Yang Liu Wei Wu Xiaoqing Li School of Mechanical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In this paper, the inverse kinematical analysis problem is investigated for a class of bio-inspired cable-driven robotic manipulator. Moreover, the modeling of the redundant cable-driven robotic manipulator is performed. In particular, the RBF neural network is adopted to deal with the unmodeled parameters uncertainties such that the inverse kinematic analysis precision can be improved. In the end, an illustrative example is presented for showing the usefulness of the developed analysis approach.

关键词： Manipulators Clustering algorithms Entropy Robot kinematics Kinematics Training Radial basis function networks

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Reveal of domain effect: How visual restoration contributes to object detection in aquatic scenes

arXiv

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arXiv 2020年

作者： Chen, Xingyu Lu, Yue Wu, Zhengxing Yu, Junzhi Wen, Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Underwater robotic perception usually requires visual restoration and object detection, both of which have been studied for many years. Meanwhile, data domain has a huge impact on modern data-driven leaning process. However, exactly indicating domain effect, the relation between restoration and detection remains unclear. In this paper, we generally investigate the relation of quality-diverse data domain to detection performance. In the meantime, we unveil how visual restoration contributes to object detection in real-world underwater scenes. According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy;2) low-quality domain leads to higher generalization ability in cross-domain detection;3) low-quality domain can hardly be well learned in a domain-mixed learning process;4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy;5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes. Finally, as an illustrative example, we successfully perform underwater object detection with an aquatic robot. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Output Feedback control for MIMO systems with Disturbances Compensation

Output Feedback Control for MIMO systems with Disturbances C...

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European control Conference (ECC)

作者： D.E. Konovalov S.A. Vrazhevsky I.B. Furtat A.S. Kremlev Martin Cech Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia The Laboratory ”Control of Complex System” Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences (IPME RAS) St. Petersburg Russia Pilsen — NTIS - European Center of Excellence University of West Bohemia

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

The paper deals with the novel feedback output control law for multiple-input multiple-output (MIMO) plant with mismatched bounded disturbances. The plant model is described by linear and Lipshitz nonlinear terms. The control law is based on the auxiliary loop method for disturbance estimation and modified backstepping method for control under mismatched disturbances. Differently from existing results based on backstepping method the proposed algorithm allows to reduce the accuracy in steady state, while the existing auxiliary loop based algorithm are feasible under matched disturbances. The results in the England Power System Benchmark illustrate the efficiency of the proposed scheme.

关键词： Backstepping Steady-state control systems Mathematical model Attenuation MIMO communication Estimation

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Bio-inspired Modeling and Position control for Pneumatic Artificial Muscle

Bio-inspired Modeling and Position Control for Pneumatic Art...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hang Fu Yang Liu Jiahao Chen School of Mechanical Engineering University of Science and Technology Beijing Beijing P. R. China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P. R. China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

In this paper, the modeling and position control problem of a class of pneumatic artificial muscle (PAM) is investigated for the bio-inspired robotic manipulator. The system model of PAM is firstly developed based on experiment data identification. Then, the improved particle swarm optimization (PSO) control parameter tuning method is established for the effective position control of PAM. In particular, the concept of information entropy is used during the optimal procedure. In the end, the simulation results are given for demonstrating the effectiveness of the proposed design and control approach.

关键词： Simulation Position control Muscles Particle swarm optimization Information entropy Tuning Optimization

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Micro/nanoliter droplet extraction by controlling acoustic vortex with miniwatt

arXiv

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arXiv 2021年

作者： Zhang, Han Yang, Jun Zhou, Yun Zheng, Jianfeng Cheng, Yong Bai, Bichao Zhang, Guoxin Lv, Yisheng CAS Key Laboratory of Noise and Vibration Institute of Acoustics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The National Center for Nanoscience and Technology Beijing100190 China School of Mechanical Engineering Changzhou University Changzhou213164 China Institute of Mechanics School of Civil Engineering & Mechanics Yanshan University Qinhuangdao066004 China Al-ion Battery Research Center Department of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266580 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Micro/nanoliter droplet is capable of achieving versatile applications with tiny volume and substantial surface energy, which is a big plus over bulk liquid. Yet, the contradiction of elaborate manipulation and enough power is still a challenge. Here, we unleash the potential of our miniwatt aspirators pumping up liquid and creating droplets with the help of acoustic vortex beams, inspired by the power mechanism that spirals are significant for most mollusks that live in water. These droplet aspirators produce very large interface deformations by small radiation pressures with orbit angular momentum from spiral-electrode transducers. The precisely contactless manipulation of physical, chemical and biological objects at micrometric down to nanometric scales, promises tremendous development in fields as diverse as microrobotics, nanoreactors, or nanoassemblies. Copyright © 2021, The Authors. All rights reserved.

关键词： Vortex flow

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Development of Sheet Palletizing Manipulator Based on Multi-link Mechanism

Research Square

引用

Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

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