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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shanying Zhu Jinming Xu Cailian Chen Xinping Guan Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781479999897

In this paper, we consider a simple model of distributed sensor fusion problem in sensor networks with asymmetric links, where the common goal is linear parameter estimation. For the realistic scenario of bandwidth-constrained networks, we propose a least square approach, based on distributed quantized consensus algorithms, to compute the ideal centralized sample mean estimate. Analytical results show that the proposed approach is effective in smearing out the quantization errors, and outperforms the centralized approaches with respect to the estimation performance. Simulation results are provided to validate the analytical results.

关键词： Quantization (signal) Sensor fusion Bandwidth Distributed algorithms Convergence Estimation Probabilistic logic

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Constrained Energy Efficiency Optimization for Robotic Manipulators Using Neuro-dynamics Approach 34

Constrained Energy Efficiency Optimization for Robotic Manip...

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第三十四届中国控制会议

作者： XU Peng WANG Liyang LI Zhijun SU Chun-Yi The Key Lab of Autonomous System and Network Control Ministry of Education and College of Automation Science and Engineering South China University of Technology Department of Electronic Engineering Shunde Polytechnic

ISBN: (纸本)9781467374439

In the paper, a neurodynamic-based energy efficiency optimization strategy(NEE-OS) is proposed to decrease the energy expenditure of robotic manipulators. Different from the traditional approaches, the proposed NEE-OS integrates several necessary constrains from the environment and the robotic manipulator, which could affect energy consumptions of the robotic manipulator in engineering applications to a large extent. To handle the constraints formulated as equalities and inequalities, the energy efficiency optimization problem is converted into a constrained quadratic programming(QP) problem, which is solved using a linear variable inequality-based primal-dual neural network(PDNN) efficiently.

关键词： robotic manipulator,energy optimization,quadratic programming,neural network

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Attitude synchronization of multiple rigid bodies in SE(3) over proximity networks

Attitude synchronization of multiple rigid bodies in SE(3) o...

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第35届中国控制会议

作者： Juan Deng Zhixin Liu Lin Wang John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceCAS Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Electrical and Computer Engineering and the Institute for Systems Research University of MarylandCollege Park

ISBN: (纸本)9781509009107

This paper investigates the attitude synchronization of a group of moving rigid bodies whose states including positions and rotation matrices evolve in the Lie group SE(3).The commonly used circular neighborhood is adopted,and the neighbor graphs are described by proximity *** order to avoid introducing discussions on the abrupt changes of neighbor graphs,a dwell time is *** distributed sampled-data control law for the angular velocity is *** the assumptions on the initial states,we provide sufficient conditions for the attitude synchronization of multiple rigid body *** with the existing results,our condition depends only on the neighborhood radius,the moving velocity and the dwell time.

关键词： rigid body SE(3) attitude synchronization sampled-data control proximity network

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Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES

arXiv

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arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

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Event-triggered consensus tracking for multi-agent systems with nonlinear uncertainty dynamics

Event-triggered consensus tracking for multi-agent systems w...

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第35届中国控制会议

作者： Xuhui Li Bohui Wang Jingcheng Wang Bin Zhang Hai Lin Xiaocheng Li the National Engineering and Research Center for Commercial Aircraft Manufacturing Commercial Aircraft Corporation of China Ltd the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China the Department of Electrical Engineering University of South Carolina

ISBN: (纸本)9781509009107

This paper addresses the event-triggered consensus tracking for general linear multi-agent systems with nonlinear uncertainty dynamics under directed communication *** proposed event-triggered consensus strategy relaxes the assumptions in existing works that the control gain and local inputs must be *** the consensus tracking condition being satisfied,the cooperative control law with bounded parameter is developed based on the intermittent *** shows that if the sampling condition,in terms of LMIs,is satisfied,the resulting closed-loop general linear multiagent uncertain systems will achieve the *** results are provided to verify the proposed approach.

关键词： Index Terms-multi-agent systems event-triggered consensus tracking uncertainty intermittent samples

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A robust terrain-based road vehicle localization algorithm

A robust terrain-based road vehicle localization algorithm

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IEEE Symposium on Intelligent Vehicle

作者： Tianyi Li Ming Yang Xujin Zhou Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

Terrain-based localization is an alternate to the global positioning system (GPS) in signal blocked areas. However, terrain-based localization technique may suffer from low accuracy or even fail when brake vibration occurs. This paper presents a real-time algorithm for vehicle localization which is robust against brake vibration. The input includes a reference map of pitch difference and measurements from rear wheel encoders and inertial measurement units (IMU). This method consists of two steps. In the first step, terrain map is generated using pitch difference at equidistant intervals. After that, the Bayesian inference and particle filters are adopted in the second step to identify the vehicle location during travel. To enhance system stability, we propose dynamic distributions of filter variances according to acceleration input. Experimental results demonstrate that the localization method with dynamic distributions can localize the vehicle quickly with high accuracy even when a quite severe shuddering happens.

关键词： Vehicles Roads Brakes Acceleration Heuristic algorithms Vibrations Standards

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Iterative learning predictive control for urban drainage systems 34

Iterative learning predictive control for urban drainage sys...

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第三十四届中国控制会议

作者： CUI Yiqun JIN Mengke LI Dewei XI Yugeng CEN Lihui Department of Automation Shanghai Jiao Tong UniversityShanghai Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Automation Science and Technology School of Information Science and EngineeringCentral South University

ISBN: (纸本)9781467374439

Urban drainage system is important for a modern city. This paper considers the control problem of an urban drainage system when a storm happens. By analyzing a storm, we see that the procedure of every storm is similar and its approximate procedure can be divided into three phases according to the change trend of rainfall. Therefore, we introduce the iterative learning control(ILC) into the urban drainage system and present a method of ILC based model predictive control(MPC). A 2D model based on a simpli ed model of drainage system is adopted. The procedure of a storm is divided into three phases and the ILC based MPC is designed for each phase by regarding it as a similar batch process. The handle of different length of phases is also considered. Since the learning based on the historic results is utilized, the proposed control method can improve the control result storm by storm. The numerical examples verify the effectiveness of the proposed design.

关键词： Predictive control,Learning control,Urban Drainage system

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A Sophisticated RMPC Design for LPV systems based on the mixed multi-step feedback control 34

A Sophisticated RMPC Design for LPV Systems based on the mix...

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第三十四届中国控制会议

作者： ZHENG Pengyuan LI Dewei XI Yugeng LI Xiaoli College of Automation Engineering Shanghai University of Electric PowerShanghai Engineering Research Center of Intelligent Management and Control for Power Process Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing Ministry of Education School of Information Science and Technology Donghua University

ISBN: (纸本)9781467374439

This paper studies a sophisticated robust model predictive control(RMPC) design for linear parameter varying(LPV)systems with the varying parameter unmeasurable. Based on a mixed multi-step feedback control, a sophisticated control design which utilizes both multi-step control sets and parameter-dependent feedback control is developed to introduce more freedom and thus reduces the conservativeness. The proposed method achieves good control performance with guaranteed robust and stability properties, which is verified by simulation examples.

关键词： Linear parameter varying systems,Robust model predictive control,Parameter-dependent feedback control,Mixed multi-step feedback control,Linear matrix inequalities

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State estimation with quantized innovations in wireless sensor networks: Gaussian mixture estimator and posterior Cramér–Rao lower bound

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Chinese Journal of Aeronautics 2015年第6期28卷 1735-1746页

作者： Zhang Zhi Li Jianxun Liu Liu Liu Zhaolei Han Shan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Nanjing Research Institute of Electronics Technology Engineering Research Center of Wideband Wireless Communication Technology Ministry of EducationNanjing University of Posts and Telecommunications

Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.

关键词： Posterior Cramer-Rao lower bounds Quantiation State estimation Target tracking Wireless sensor network

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Explicit adaptive synchronization of multi-agent uncertain systems with heterogeneous dynamic networks

Explicit adaptive synchronization of multi-agent uncertain s...

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American control Conference (ACC)

作者： Bohui Wang Yingguan Wang Bin Zhang Fengrong Li Jingcheng Wang Hai Lin Xiaocheng Li Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Key Laboratory of Wireless Sensor Network & Communication Chinese Academy of Sciences Shanghai China Department of Electrical Engineering University of South Carolina Columbia SC USA

ISBN: (纸本)9781467386838

This paper proposes an explicit adaptive algorithm to solve the problem of heterogeneous dynamic synchronization of multi-agent uncertain systems. Different from the existing works, the proposed algorithm depends on less control parameters and achieves zero synchronization error between the agents of heterogeneous uncertain dynamic networks and virtual target. By introducing appropriate assumptions, a class of explicit adaptive synchronization protocols is designed based on a combination of the virtual target's states, distributed feedback gain, and the limited neighboring agents' information. By using Lyapunov function approach, it is proved that the explicit adaptive synchronization problem of closed-loop multi-agent uncertain systems with heterogeneous dynamic networks can be solved, if the control parameters of the explicit adaptive synchronization algorithm are suitably selected. Finally, an example is given to demonstrate the proposed theory.

关键词： Synchronization Heuristic algorithms Uncertain systems Adaptive systems Protocols Eigenvalues and eigenfunctions Laplace equations

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