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International Symposium on Communications control and Signal Processing (ISCCSP)

作者： Themistoklis Charalambous Christoforos N. Hadjicostis Mikael Johansson Department of Automatic Control Royal Institute of Technology (KTH) Stockholm Sweden Department of Electrical and Computer Engineering University of Illinois Nicosia IL USA

We address the weight-balancing problem for a distributed system whose components (nodes) can exchange information via interconnection links (edges) that form an arbitrary, possibly directed, communication topology (digraph). A weighted digraph is balanced if, for each node, the sum of the weights of the edges outgoing from that node is equal to the sum of the weights of the edges incoming to that node. Weight-balanced digraphs play a key role in a variety of applications, such as coordination of groups of robots, distributed decision making, and distributed averaging which is important for a wide range of applications in signal processing. We propose a distributed algorithm for solving the weight balancing problem in a minimum number of iterations, when the weights are nonnegative real numbers. We also provide examples to corroborate the proposed algorithm.

关键词： Vectors Distributed algorithms Polynomials Topology Signal processing algorithms Educational institutions Convergence

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On the convergence of an alternating direction penalty method for nonconvex problems

On the convergence of an alternating direction penalty metho...

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Asilomar Conference on Signals, Systems & computers

作者： S. Magnússon P. C. Weeraddana M. G. Rabbat C. Fischione Department of Automatic Control KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering McGill University Montréal Canada

This paper investigates convergence properties of scalable algorithms for nonconvex and structured optimization. We consider a method that is adapted from the classic quadratic penalty function method, the Alternating Direction Penalty Method (ADPM). Unlike the original quadratic penalty function method, in which single-step optimizations are adopted, ADPM uses alternating optimization, which in turn is exploited to enable scalability of the algorithm. A special case of ADPM is a variant of the well known Alternating Direction Method of Multipliers (ADMM), where the penalty parameter is increased to infinity. We show that due to the increasing penalty, the ADPM asymptotically reaches a primal feasible point under mild conditions. Moreover, we give numerical evidence that demonstrates the potential of the ADPM for computing local optimal points when the penalty is not updated too aggressively.

关键词： Optimization Convergence Linear programming Couplings Approximation algorithms Signal processing algorithms Signal processing

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Analysis, filtering, and control for takagi-sugeno fuzzy models in networked systems

Abstract and Applied Analysis

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Abstract and Applied Analysis 2015年第1期2015卷

作者： Zhang, Sunjie Wang, Zidong Hu, Jun Liang, Jinling Alsaadi, Fuad E. School of Information Science and Technology Donghua University Shanghai 200051 China Department of Computer Science Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Communication Systems and Networks (CSN) Research Group Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China Department of Mathematics Southeast University Nanjing 210096 China

The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out. © 2015 Sunjie Zhang et al.

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A Model-based Demand-balance control for Complex Urban traffic Networks

A Model-based Demand-balance Control for Complex Urban traff...

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17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems engineering Conference 2014

作者： Shu Lin Qing-Jie Kong School of Computer and Control Engineering University of Chinese Academy of Sciences Qingdao Academy of Intelligent Industries the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Traffic control is an effective and efficient method for the problem of traffic *** complex urban traffic networks,it is necessary to design a high-level controller to regulate the traffic *** the parallel control framework for complex traffic networks,we design a demand-balance MPC controller based on the MFD-based multi-subnetwork model,which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the *** transferring traffic flows among subnetworks are indirectly controlled by the demand-balance MPC controller,and a global optimality can be achieved for the entire traffic *** simulation results show the effectiveness of the proposed controller in improving the network traffic throughput.

关键词： A Model-based Demand-balance control for Complex Urban traffic Networks MFD Model

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Integrated estimation of vehicle states, tire forces, and tire-road friction coefficients

Integrated estimation of vehicle states, tire forces, and ti...

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International Conference on control, Automation and Systems ( ICCAS)

作者： Tesheng Hsiao Jing-Yuan Lan Hanping Yang Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electrical Control Engineering National Chiao Tung University Hsinchu Taiwan Industrial Technology Research Institute Hsinchu Taiwan

Information about the vehicle states (e.g. the sideslip angle and the longitudinal velocity), tire forces, and tire-road friction coefficients is crucial for advanced vehicular active safety systems. In particular, the latest high-performance control system based on optimally distributing and controlling all tire forces requires feedback of the tire forces and the tire-road friction coefficient of each individual tire. Therefore, estimating all or parts of the vehicle/tire/road states from available sensor measurements in production vehicles has long been an active research topic in both academia and industry. Despite recent advances in automotive estimation technologies, it remains an open question on identifying the tire-road friction coefficient of each individual tire. This paper proposes an integrated estimation system that provides accurate estimates of the vehicle/tire/road states from available sensor measurements. More importantly, the tire-road friction coefficient of each tire can be separately estimated. The performance of the proposed estimation system is verified by simulations based on a complex nonlinear vehicle/ tire model.

关键词： Estimation Vehicles Tires

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Application of derivative-free multi-objective algorithms to reliability-based robust design optimization of a high-speed catamaran in real ocean environment 4

Application of derivative-free multi-objective algorithms to...

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4th International Conference on engineering Optimization, ENGOPT 2014

作者： Pellegrini, R. Campana, E.F. Diez, M. Serani, A. Rinaldi, F. Fasano, G. Iemma, U. Liuzzi, G. Lucidi, S. Stern, F. CNR-INSEAN National Research Council-Marine Technology Research Institute Rome Italy Department of Mathematics Università di Padova Padua Italy Department of Management Università Ca’Foscari Venice Italy Department of Engineering Università Roma Tre Rome Italy Department of Computer Control and Management Engineering Università La Sapienza Rome Italy IIHR-Hydroscience and Engineering The University of Iowa Iowa City United States

ISBN: (纸本)9781138027251

A Reliability-Based Robust Design Optimization (RBRDO) for ship hulls is presented. A real ocean environment is considered, including stochastic sea state and speed. The optimization problem has two objectives: (a) the reduction of the expected value of the total resistance in waves and (b) the increase of the ship operability (reliability). Analysis tools include a URANS solver, uncertainty quantification methods and metamodels, developed and validated in earlier research. The design space is defined by an orthogonal four-dimensional representation of shape modifications, based on the Karhunen-Loève expansion of free-form deformations of the original hull. The objective of the present paper is the assessment of deterministic derivativefree multi-objective optimization algorithms for the solution of the RBRDO problem, with focus on multiobjective extensions of the Deterministic Particle Swarm Optimization (DPSO) algorithm. Three evaluation metrics provide the assessment of the proximity of the solutions to a reference Pareto front and their wideness. © 2015 Taylor & Francis Group, London.

关键词： Hulls (ship)

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ESC-MRAC of MIMO systems for constrained robotic motion tasks in deformable environments

ESC-MRAC of MIMO systems for constrained robotic motion task...

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European control Conference (ECC)

作者： Vasiliki Koropouli Azwirman Gusrialdi Dongheui Lee Institute of Automatic Control Engineering Technische Universität München München Germany Dept. of Electrical Engineering and Computer Science University of Central Florida USA

ISBN: (纸本)9781479947287

Performance of constrained movements in multiple directions of a workspace simultaneously and in presence of uncertainty is a great challenge for robots. Achieving such tasks by employing control policies which are fully determined a priori and do not take into account the system uncertainty can cause undesired stress on the robot end-effector or the environment and result in poor performance. Instead, a sophisticated control policy is required, which can adjust to the varying conditions of a task while taking into account the coupling of motion dynamics between different directions of movement. To this aim, in this paper, we propose a MIMO Extremum Seeking control (ESC)-Model Reference Adaptive control (MRAC) approach with the view of executing fine motion tasks in presence of uncertain task dynamics. ESC enhances robustness of the system to non-parametric uncertainties compared to single MRAC. The proposed approach ensures state tracking as well as optimization of a global state-dependent cost criterion in all directions of movement. We evaluate our approach in simulations and in a real-world robotic engraving task.

关键词： MIMO Tracking Dynamics Vectors Uncertainty Adaptation models Robots

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset Pursuit algorithm

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Null-steering beamforming in uncertain environment

Null-steering beamforming in uncertain environment

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第26届中国控制与决策会议

作者： Jian Hou Zhiyun Lin Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control College of Electrical Engineering Zhejiang University School of Electrical Engineering and Computer Science University of NewcastleCallaghanNSW 2308Australia

In this paper, we study a transmit null-steering beamforming problem for wireless sensor networks in uncertain communication environment. Given a desired receiver direction,the system requires to avoid the unknown eavesdroppers as *** this case, we adjust both the sensors distribution and phase offsets such that beamforming is achieved at the desired direction,and meanwhile the communication of all the other directions is suppressed, which substantially reduces the risk of information theft by eavesdroppers. The Particle Swarm Optimization（PSO）algorithm is used to solve this constraint problem.

关键词： Null-steering beamforming particle swarm optimization wireless sensor networks constraint optimization.

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In-image rain wiper elimination for vision-based Advanced Driver Assistance Systems

In-image rain wiper elimination for vision-based Advanced Dr...

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International Conference on control and Automation (ICCA)

作者： Che-Tsung Lin Yu-Chen Lin Long-Tai Chen Yuan-Fang Wang Safety Sensing and Control Department Industrial Technology Research Institute Department of Automatic Control Engineering Feng Chia University Taiwan Department of Computer Science University of California Santa Barbara CA USA

This paper proposes a new algorithm to eliminate the wiper interference in a vehicle's onboard video to improve the detection rate of vision-based Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW) Systems. During raining days, the windshield wipers periodically and partially block the appearance of obstacles on the road that are to be detected in these early warning systems, and hence, impair their performance. The wiper pixels could be in-painted via (1) localizing the pixels belonging to the wipers and (2) replacing the wiper pixels with non-wiper pixels extracted from an adjacent video frame with no such blockage. Finally, since it is impossible to obtain the images captured in the same scenario with and without wiper interference, a quantitatively analyzing framework which blends wipers into non-wiper images is also proposed to quantitatively assess the detection rate of data with wipers, without wipers and with wipers but eliminated by our algorithm. The experimental results show that the wiper pixels being segmented and in-painted blend in unobtrusively with the surrounding pixels and the FCW detection rate is indeed improved.

关键词： Training Image segmentation Vehicles Interference Rain Accuracy Video sequences

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