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Distributed proportional plus second-order spatial derivative control for distributed parameter systems subject to spatiotemporal uncertainties

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NONLINEAR DYNAMICS 2014年第4期76卷 2041-2058页

作者： Wang, Jun-Wei Li, Han-Xiong Wu, Huai-Ning Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China Cent South Univ State Key Lab High Performance Complex Mfg Changsha Hunan Peoples R China Beijing Univ Aeronaut & Astronaut Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beihang Univ Beijing 100191 Peoples R China

In this paper, a robust distributed control design based on proportional plus second-order spatial derivative (P-sD) is proposed for exponential stabilization and minimization of spatial variation of a class of distributed parameter systems (DPSs) with spatiotemporal uncertainties, whose model is represented by parabolic partial differential equations with spatially varying coefficients. Based on the Lyapunov's direct method, a robust distributed P-sD controller is developed to not only exponentially stabilize the DPS for all admissible spatiotemporal uncertainties but also minimize the spatial variation of the process. The outcome of the robust distributed P-sD control problem is formulated as a spatial differential bilinear matrix inequality (SDBMI) problem. A local optimization algorithm that the SDBMI is treated as a double spatial differential linear matrix inequality (SDLMI) is presented to solve this SDBMI problem. Furthermore, the SDLMI optimization problem can be approximately solved via the finite difference method and the existing convex optimization techniques. Finally, the proposed design method is successfully applied to feedback control problem of the FitzHugh-Nagumo equation.

关键词： Distributed parameter systems Robust control Exponential stability Spatiotemporal uncertainty Linear matrix inequalities (LMIs)

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ACP based self-learning control for urban intersection

ACP based self-learning control for urban intersection

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Feng Chen Lingyun Zhu Chuyue Han Xiong Gang University of Science and Technology of China Hefei China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

The intersection models, such as delay models and queuing length models, are the foundations of optimal signal timing for urban intersection. Lack of the field data of intersection, it is highly difficult to calibrate parameters of the intersection models. Due to the effects of intersection topology, channelization and traffic conditions on these models, obviously it is impossible for single model to be suitable for optimal control of various intersections.

关键词： Silicon Optical saturation Queueing analysis Computational modeling Learning (artificial intelligence)

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Topological mapping and navigation based on visual sensor network

Topological mapping and navigation based on visual sensor ne...

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IEEE International Conference on Mechatronics and Automation

作者： Wenbo Yuan Zhiqiang Cao Min Tan Jingyu Liu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Electrical and Information Engineering Beijing jiaotong University Beijing China

ISBN: (纸本)9781479939800

In this paper, a novel topological mapping approach applied to the navigation mission is proposed based on visual sensor network. Firstly, the natural objects in the scene monitored by visual sensor network are recognized through the inference model based on structural features. Then, topological nodes are designed according to the recognized objects and their spatial structure information, and topological mapping will be fulfilled. On this basis, global localization for mobile robot in camera view can be implemented with HOG feature based detection and tracking. Finally, the robot can make motion decisions for navigation. Our approach does not rely on artificial landmarks and is tested in large-scale office scene. The effectiveness of the proposed approach is verified.

关键词： Visualization Robot sensing systems Navigation Cameras Image segmentation Feature extraction

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Prediction and Evaluation of Traffic states at Signalized Intersections

Prediction and Evaluation of Traffic States at Signalized In...

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2014 IEEE International Conference on Service Operations and Logistics,and Informatics

作者： Ji-quan Shen Qing-jie Kong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Computer Science and Technology Henan Polytechnic University Member IEEE

The parallel transportation system based on the method of ACP（Artificial systems,Computing experiments,Parallel control）will promote the level of city traffic intelligent decision and scientific management.A key problem in the system is how to design a computing experiment method to predict and evaluate the traffic state by real-time and *** paper introduces the discrete-time queuing model to analyze the traffic flow at the signalized intersection and gives the evaluation conditions of the traffic ***,the evaluation conditions are applied to judge the traffic state based on the prediction data of traffic flows from the grey *** show the method is effective and feasible.

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Computational experiments based on competitive influence diffusion model

Computational experiments based on competitive influence dif...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Kainan Cui Xiaolong Zheng The School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479960590

Understanding the strategies to optimize/suppress information spreads under intense competition could provide important insights in a broad range of settings including viral marketing, emergency response and information system design. However, most of existing studies about competitive influence diffusion mainly focus on two-information competition mechanism. To date, the competitive influence maximization problem considering the mechanism of multi-information competition is still not well studied. In this paper, we conducted computational experiments to study the competitive influence maximization with multi-information competition mechanism. By applying an information diffusion model called limited attention model (LAM), we carried on two computational experiments to validate the model and investigate the relation between seed selection methods and the properties of information cascades. Our experimental results show that 1) the LAM model could reproduce the features of empirical distribution in Chinese social media; 2) the eigenvector centrality-based heuristic is a reasonable seed selection method for competitive influence maximization problem. The results of this paper can provide significant potential implications for information system design and management.

关键词： Computational modeling Media Blogs

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CPG-based Sensory Feedback control for Bio-inspired Multimodal Swimming

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC systems 2014年第10期11卷 170-170页

作者： Wang, Ming Yu, Junzhi Tan, Min Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Sensory feedback plays a very significant role in the generation of diverse and stable movements for animals. In this paper we describe our effort to develop a Central Pattern Generator (CPG)-based sensory feedback control for the creation of multimodal swimming for a multi-articulated robotic fish in the context of neurocomputing. The proposed control strategy is composed of two phases: the upper decision-making and the automatic adjustment. According to the upper control commands and the sensory inputs, different swimming gaits are determined by a finite state machine algorithm. At the same time, the sensory feedback is exploited to shape the CPG coupling forms and control parameters. In the automatic adjustment phase, the CPG model with sensory feedback will adapt the environment autonomously. Simulation and underwater tests are further conducted to verify the presented control scheme. It is found that the CPG-based sensory feedback control method can effectively improve the manoeuvrability and adaptability of the robotic fish in water.

关键词： Central Pattern Generator (CPG) Sensory Feedback Robotic Fish Neurocomputing Swimming control

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UA-MAC: An Underwater Acoustic Channel Access Method for Dense Mobile Underwater Sensor Networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2014年第2期10卷 1-1页

作者： Lv, Chao Wang, Shuo Tan, Min Chen, Lan Chinese Acad Sci Inst Microelect Beijing 100864 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100864 Peoples R China

The long propagation delay and low data rate characteristics of acoustic communication attach many challenges to underwater wireless sensor networks. In this paper, a TDMA based underwater acoustic channel access method (UA-MAC) is proposed mainly to improve channel utilization in dense mobile underwater wireless sensor networks (MUWSN). In UA-MAC, the time slot of each node accessing to the acoustic channel is assigned according to a preverified template, which aims to minimize amount of slots so as to decrease the end-to-end delay. In addition, a piggyback based protocol is implemented to synchronize and schedule the network. The simulation results show that UA-MAC is much superior to common TDMA in throughput, channel utilization, and end-to-end delay especially in dense scenario. Based on the low-power low-cost underwater acoustic nodes we developed, the experiments were carried out to validate the proposed UA-MAC.

关键词： UNDERWATER acoustic communication WIRELESS sensor networks END-to-end delay (Computer networks) SIMULATION methods & models ENERGY consumption

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Tracking control of Discrete-Time Affine Nonlinear systems Based on Kernel-HDP Algorithm 11

Tracking Control of Discrete-Time Affine Nonlinear Systems B...

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11th World Congress on Intelligent control and Automation

作者： Tan, Fuxiao Liu, Derong Guan, Xinping Fuyang Teachers Coll Sch Comp & Informat Fuyang Anhui Peoples R China Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781479958252

In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods and approximately linear dependence (ALD) analysis into the critic module of ADP for the optimal tracking controller design. An ADP algorithm based on sparse kernel learning and heuristic dynamic programming (HDP) is proposed, that is, kernel HDP (KHDP). Based on KHDP, an experiment is established. By simulation, the effectiveness of proposed algorithm is demonstrated.

关键词： Dynamic programming

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A Stochastic Model for Budget Distribution over Time in Search Advertisements

A Stochastic Model for Budget Distribution over Time in Sear...

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Qin, Rui Yuan, Yong Li, Juanjuan Yang, Yanwu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

ISBN: (纸本)9781479960583

In search advertisements, advertisers have to seek for an effective allocation strategy to distribute the limited budget over a series of sequential temporal slots (e.g., days). However, advertisers usually have no sufficient knowledge to determine the optimal budget for each temporal slot, because there exist much uncertainty in search advertising markets. In this paper, we present a stochastic model for budget distribution over a series of sequential temporal slots under a finite time horizon, assuming that the best budget is a random variable. We study some properties and feasible solutions for our model, taking the best budget being characterized by either normal distribution or uniform distribution, respectively. Furthermore, we also make some experiments to evaluate our model and identify strategies with the real-world data collected from practical advertising campaigns. Experimental results show that a) our strategies outperform the baseline strategy that is commonly used in practice;b) the optimal budget is more likely to be normally distributed than uniformly distributed.

关键词： search advertisement budget distribution optimal budget stochastic programming budget constraint

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Time Critical Disinformation Influence Minimization in Online Social Networks

Time Critical Disinformation Influence Minimization in Onlin...

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IEEE Joint Intelligence and Security Informatics Conference (JISIC)

作者： Chuan Luo Kainan Cui Xiaolong Zheng Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China The School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China Department of Management Information Systems University of Arizona Tucson USA

If a piece of disinformation released from a terrorist organization propagates on Twitter and this adversarial campaign is detected after a while, how emergence responders can wisely choose a set of source users to start the counter campaign to minimize the disruptive influence of disinformation in a short time? This practical problem is challenging and critical for authorities to make online social networks a more trustworthy source of information. In this work, we propose to study the time critical disinformation influence minimization problem in online social networks based on a continuous-time multiple campaign diffusion model. We show that the complexity of this optimization problem is NP-hard and provide a provable guaranteed approximation algorithm for this problem by proving several critical properties of the objective function. Experimental results on a sample of real online social network show that the proposed approximation algorithm outperforms various heuristics and the transmission temporal dynamics knowledge is vital for selecting the counter campaign source users, especially when the time window is small.

关键词： Radiation detectors Social network services Greedy algorithms Optimization Linear programming Information processing Minimization

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