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IEEE Region 10 International Conference TENCON

作者： Yuanlong Xie Liquan Jiang Jie Meng Shuting Wang Shiqi Zheng Hao Wu School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728184555

ISBN: (纸本)9781728184562

Four-wheel-steering mobile robots (FMR) are extensively-adopted for manufacturing applications. However, the robust trajectory tracking of FMR is hard to achieve due to the complex disturbances. For addressing the challenge, an asymmetric barrier function-based adaptive control (ABFAC) method is presented here for obtaining a robust direct yaw moment control scheme for the FMR. The praiseworthy features of the proposed ABFAC method are twofold: (a) It does not need the information concerning the upper boundary information of the unknown uncertainties; (b) By introducing the barrier function, the output signals are guaranteed to be within the desired neighborhood since the sliding mode gains will grow once the disturbance derivatives increase. In this context, the proposed ABFAC method drives the resultant trajectory tracks the variation tendency of the disturbances, guaranteeing robust closed-loop responses. The global stability of the FMR can be ensured theoretically. Finally, experimental results of real-time FMR are provided to validate the applicability of our ABFAC method.

关键词： Asymptotic stability Trajectory tracking Stability analysis Trajectory Mobile robots Adaptive control Convergence

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Performance monitoring and parameter optimization of fuzzy controller in combustion process of coke Oven

Performance monitoring and parameter optimization of fuzzy c...

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Asian control Conference

作者： Qi Lei Di Zhu Xu-Zhi Lai Min Wu School of Information Science and Engineering University of Central South University Changsha China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

The controller parameters can not perform well in the combustion process of coke oven, caused by the fluctuating calories of fuel gas, the changes of coking time and many other exogenous disturbances. In this paper, an optimal method based on performance monitoring by using the trend extraction method, is raised to adjust the parameters of the controllers in the combustion process of coke oven. Firstly, because of the mixture of different kinds of error, the trend extraction method is used to separate trend component from original signal. Secondly, the monitoring indices are designed to examine whether the monitored controller parameters are suitable in the current situation. Then, the performance monitoring result is utilized to choose the decision vector of optimization and determine whether optimization is needed. Finally, simulation results demonstrate that our method performs well in the optimization for combustion process of coke oven in spite of massive changes in the calories of fuel gas.

关键词： Monitoring Combustion Market research Optimization Process control Ovens Heating systems

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Stability Analysis of Discrete T-S Fuzzy systems with Time-Varying Delay Using an Extended Reciprocally Convex Matrix Inequality

Stability Analysis of Discrete T-S Fuzzy Systems with Time-V...

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Chinese control Conference (CCC)

作者： Bi-Ying Chen Fei Long Chuan-Ke Zhang Yong He Hui-Qin Xiao Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China College of Electrical and Information Engineering Hunan University of Technology Zhuzhou P. R. China

This paper concerns the stability analysis of discrete Takagi-Sugeno fuzzy systems with time-varying delays. A suitable Lyapunov-Krasovskii functional is constructed, and an extended reciprocally convex matrix inequality is used to estimate the forward difference of the functional. As a result, a less conservative stability criterion is obtained. Finally, a numerical example is given to demonstrate the advantage of the proposed method.

关键词： Delays Linear matrix inequalities Fuzzy systems Time-varying systems Stability criteria Symmetric matrices

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Aeromagnetic compensation of Rotor UAV Based on Least Squares

Aeromagnetic compensation of Rotor UAV Based on Least Square...

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第37届中国控制会议

作者： Han Li Jian Ge Haobin Dong Xiangyu Qiu Wang Luo Huan Liu Zhiwen Yuan Jun Zhu Haiyang Zhang School of Automation China University of Geosciences School of Automation China University of Geosciences Wuhan Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Institute of Geophysics & Geomatics China University of GeosciencesWuhan

Aeromagnetic survey is widely used in geophysical exploration because of large exploration range and little limitations on geographical environment. Aiming at the problem using manned aircraft in aeromagnetic survey, an aeromagnetic system of rotor unmanned aerial vehicle(UAV) based on Overhauser sensor is established. It can carry out high-precision geomagnetic measurement with small magnetic interference and omidirection. Firstly, the magnetic interference of rotor UAV is analyzed and the mathematical model is established for software compensation. Next, aeromagnetic magnetometer based on Overhauser sensor is developed. Finally, the least squares is used to estimate the coefficients. The necessity and superiority of allowing for the eddy-current magnetic field is verified by comparing the fitting results. Moreover, the flight test was carried out and the results showed that the improvement ratio was 6.86. The compensation effect of magnetic interference is verified and magnetic anomaly signal is extracted successfully.

关键词： aeromagnetic compensation Overhauser sensor rotor unmanned aerial vehicle

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Development of A Customizable Real-time Monitoring system for Networked control systems

Development of A Customizable Real-time Monitoring System fo...

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Chinese control Conference

作者： Haoran Tan Zhiwu Huang Min Wu School of Information Science and Engineering Central South University Changsha P. R. China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan P. R. China

The design and implementation of a monitoring system for networked control systems (NCSs) are introduced in this paper. The monitoring system is developed in the .NET framework using C# according to the C/S architecture which allows the user to monitor network nodes without geographical limitations. The widgets used to display come from the National Instruments (NI) suit and are integrated in the system by reconstructing it. The user is able to design a monitoring interface freely by creating and customizing widgets dynamically. During the monitoring, the display value of the widget is synchronized with parameters and signals of NCSs through receiving data asynchronously from network notes. In particular, all elements of the matrix widely used in NCSs are supported for monitoring. The functionality and feasibility of the designed monitoring system are demonstrated via a networked control experiment. In addition, the saved data can be used in data driven control and model identification.

关键词： Monitoring Graphical user interfaces control systems Communication channels Mice Protocols Real-time systems

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Crowdsensing game with demand uncertainties: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Zhan, Yufeng Xia, Yuanqing Zhang, Jiang Li, Ting Wang, Yu School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Lab Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Learning Hidden Markov Models for Linear Gaussian systems with Applications to Event-based State Estimation

arXiv

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arXiv 2020年

作者： Zheng, Kaikai Shi, Dawei Shi, Ling State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Longyan Li Chao Ning Haifeng Qiu Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350399677

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study.

关键词： Renewable energy sources Uncertainty Hydrogen Asia Mixture models Probabilistic logic Real-time systems

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Mechanical arm grasping and target recognition strategy based on the coarse-to-fine algorithm

Mechanical arm grasping and target recognition strategy base...

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International Conference on Information and Automation (ICIA)

作者： Songmin Jia Zengyue Ju Tao Xu Hui Zhang Xiuzhi Li College of Electronic and Control Engineering Beijing University of Technology Beijing China Ministry of Education Engineering Research Center of Digital Community Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781509041039

Target recognition is a fundamental research topic in the process of robot grasping. In this paper, we proposed an algorithm framework for object recognition based on local naive Bayes nearest neighbor with Kinect. With the emergence of local invariant feature detection, the method based on local invariant features gradually becomes the mainstream. Object recognition is realized through the feature matching of the model in the current scene and the models in the library based on local invariant property. Considering the number of models in the library may be as many as dozens or even hundreds, I divide the recognition process into coarse and fine recognition, the part of coarse recognition adopts the local naive Bayes nearest neighbor algorithm, just search for a number of the nearest neighbors of the object to be identified, it does not need to compare all models in the model library one by one, the computational complexity of the model increases with the number of models in the logarithmic growth, So we can effectively deal with the situation of large data in library. The process of fine recognition is a process of layers of verification, it mainly includes geometric verification, pose verification, projection verification, the model with the most matching points will be used as the final recognition result. In the end, a variety of performances were tested on the garage willow database and the grasping experiments of the robot arm demonstrate the superiority of my proposed method.

关键词： Feature extraction Object recognition Grasping Three-dimensional displays Robot sensing systems Libraries

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Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Differentially Private Distributed Mismatch Tracking Algorit...

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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan 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 School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against attackers with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of resources while preserving privacy. Adopting constant stepsizes, the linear convergence property of diff-DMAC in mean square is established under the standard assumptions of Lipschitz smoothness and strong convexity. Moreover, it is theoretically proven that the proposed algorithm is ϵ-differentially private. And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification.

关键词： Privacy Differential privacy Cost function Resource management Standards Convergence

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