Abstract This paper considers the stability and stabilization of a class of switched polynomial nonlinear systems. Firstly, based on time-dependent switching control and a set of auxiliary matrices, new explicit suffi...
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Abstract This paper considers the stability and stabilization of a class of switched polynomial nonlinear systems. Firstly, based on time-dependent switching control and a set of auxiliary matrices, new explicit sufficient conditions are developed to ensure asymptotical stability of the studied class of systems. Moreover, switching rule design is considered for this class of systems with state-dependent switching control. Sufficient conditions for stability and stabilization are given in polynomial matrix inequalities (PMIs), and these conditions can be verified by sum of squares technique which solves linear matrix inequality feasibility problem in essence. A numerical example is provided to illustrate the effectiveness of the proposed methods.
Abstract The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort with indoor environmental and human factors considered. However, PMV is difficult to control as its mathematical model...
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Abstract The predicted mean vote (PMV) index is widely used to evaluate the indoor thermal comfort with indoor environmental and human factors considered. However, PMV is difficult to control as its mathematical model is complicated and uncertain. Moreover, spatial distributions of environmental factors are neglected by using one PMV index in a room. In this paper, Computational Fluid Dynamics (CFD) technology is applied for simulation of the environmental factors in order to accurately describe PMV index. To deal with measurement noises or other system uncertainties, an Interval type-2 fuzzy model of PMV is developed and a new GK-GA-based modeling method is proposed. The essential issue of type-2 fuzzy modeling lies in the appropriate choice of secondary membership functions. In this study, the primary membership function is gained through G-K algorithm, and the secondary membership function is determined through Genetic Algorithm (GA). The consequent of the fuzzy rules is identified by least squared algorithm. Simulation results show that the type-2 fuzzy model is favorable to minimize the influence of uncertainties and the proposed method is effective and with good accuracy.
作者：
Qing SongXiaofan WangDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
Abstract The problem of efficient path computation arises in several applications such as intelligent transportation and network routing. Although various algorithms exist for computing shortest paths, their heavy pre...
Abstract The problem of efficient path computation arises in several applications such as intelligent transportation and network routing. Although various algorithms exist for computing shortest paths, their heavy precomputation/storage costs and/or query costs hinder their application to real-time routing. By detecting hierarchical community structure in road networks, we develop a community-based hierarchical graph model that supports efficient path computation on large road networks. We then propose a new hierarchical routing algorithm that can significantly reduce the search space over the conventional algorithms with acceptable loss of accuracy. Finally, we evaluate our approach experimentally using a large, real-world road network to show the gain in performance.
作者：
Zhi ZhangLi-Sheng HuDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
Abstract During the last two decades, performance assessment for controlsystems has been receiving wide attention. The key step is to describe and estimate the benchmark. However, the issue for more complex control s...
Abstract During the last two decades, performance assessment for controlsystems has been receiving wide attention. The key step is to describe and estimate the benchmark. However, the issue for more complex controlsystems still remains open. This paper is concerned primarily with the estimation of the performance benchmark in the tensor space, and explores to extend traditional methods to the complex system. An estimation procedure based on higher-order singular value decomposition is proposed. Numerical examples shows the effectiveness of the proposed method.
Global Positioning system (GPS)-equipped probe vehicles have been one of the principal techniques in detection of traffic flow. In order to guide the application of the different methods using data from GPS probe vehi...
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In this paper, we address the design of decentralized controller for connectivity-preserving flocking without velocity measurement. An output vector based on neighbors' position information alone is constructed to...
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This paper examines robust partially mode delay dependent H ∞ output feedback controller design for discrete-time systems with random communication delays. A finite state Markov chain with partially known transition ...
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Finding the closest points in the two normal convex hulls (NCH) gives an intuitive geometry explanation to support vector machine (SVM) in hard-margin case. However, the reduced convex hull (RCH) introduced in soft-ma...
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Finding the closest points in the two normal convex hulls (NCH) gives an intuitive geometry explanation to support vector machine (SVM) in hard-margin case. However, the reduced convex hull (RCH) introduced in soft-margin case is difficult to understand and explain because it cuts off some boundary points of NCH, which probably become support vectors. This paper proposes a soft-margin SVM model still based on NCH, named as L2-SVC-NCH. L2-SVC-NCH can be interpreted as finding two closest points in the two NCHs on a feature space and the punishment parameter C for empirical risk can be viewed as a trade off between the ordinary kernel function and Kronecker delta kernel function, which makes that L2-SVC-NCH is more perfect and easier to understand and explain than the SVM model based on RCH. In addition, L2-SVC-NCH obtains many additional advantages, such as the good feasible region, the strictly convex objective function and many optional geometric algorithms. The relationships between L2-SVC-NCH and some commonly used SVM models are also illustrated. The comparative experiments on five benchmark datasets show that L2-SVC-NCH is effective and competitive.
作者：
Dewei LiYugeng XiDepartment of Automation
Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiaotong University Shanghai China
For the polytopic uncertain systems with unmeasurable system states, this paper considers the synthesis of robust model predictive control (RMPC). A category of dynamic output feedback is adopted as the control strate...
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ISBN:
(纸本)9781612844879
For the polytopic uncertain systems with unmeasurable system states, this paper considers the synthesis of robust model predictive control (RMPC). A category of dynamic output feedback is adopted as the control strategy. Compared with common dynamic output feedback approach, the adopted approach adds some new freedom, which is optimized online by RMPC with some parameters of the controller given in advance. The proposed RMPC is proven to be robustly stable and recursively feasible. And the system constraints can be satisfied. Meanwhile, in order to reduce the online computational complexity of RMPC, an off-line version of the proposed output feedback RMPC is also developed. This makes the design more practical.
Almost all existing social learning models assume that each agent can perceive her private signal which is used in updating her belief. In this work, we assume that there are some uninformed agents in the network whic...
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Almost all existing social learning models assume that each agent can perceive her private signal which is used in updating her belief. In this work, we assume that there are some uninformed agents in the network which cannot observe their private signals and update their beliefs just based on the beliefs of their neighbors. We prove that under mild assumptions, even one informed agent is enough to lead all agents in the network eventually learn the true state of the world almost surely. Furthermore, we show through simulation that in a heterogeneous undirected network, it is more efficient to have a few hub agents as the informed agents which can observe their signals, and the convergence speed is almost the same as that when all agents are informed agents.
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