Cailian Chen

Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays

This study introduces a guaranteed cost control method for nonlinear systems with time-delays which can be represented by Takagi-Sugeno (T-S) fuzzy models with time-delays. The state feedback and generalized dynamic output feedback approaches are considered. The generalized dynamic output feedback controller is presented by a new fuzzy controller architecture which is of dual indexed rule base. It considers both the dynamic part and the output part of T-S fuzzy model which guarantees that the controller without any delay information can stabilize time-delay T-S fuzzy systems. Based on delay-dependent Lyapunov functional approach, some sufficient conditions for the existence of state feedback controller are provided via parallel distributed compensation (PDC) first. Second, the corresponding conditions are extended into the generalized dynamic output feedback closed-loop system via so-called generalized PDC technique. The upper bound of time-delay can be obtained using convex optimization such that the system can be stabilized for all time-delays whose sizes are not larger than the bound. The minimizing method is also proposed to search the suboptimal upper bound of guaranteed cost function. The effectiveness of the proposed method can be shown by the simulation examples.

H/sub /spl infin// controller synthesis of fuzzy dynamic systems based on piecewise Lyapunov functions and bilinear matrix inequalities

This work presents an H/sub /spl infin// controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. It is shown that a piecewise continuous Lyapunov function can be used to establish the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems and the control laws can be obtained by solving a set of bilinear matrix inequalities (BMIs). Two examples are given to illustrate the application of the proposed methods.

Delay-Dependent Stability Analysis and Controller Synthesis for Discrete-Time T–S Fuzzy Systems With Time Delays

Based on a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DPLKF), this paper presents delay-dependent stability analysis and synthesis methods for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delays. It is shown that the stability and stabilization with some required performance can be established for the closed loop control system if there exists a DPLKF and that the DPLKF and the corresponding controller can be obtained by solving a set of linear matrix inequalities (LMIs). New algorithms have also been developed to obtain the maximum value of the allowable constant delay and the suboptimal performance upper bound. An example is finally presented to demonstrate the efficiency and advantage of the proposed methods

Distributed optimal consensus filter for target tracking in heterogeneous sensor networks

In this paper, we address the problem of distributed consensus filter design for target tracking problems using heterogeneous sensor networks with two types of sensors. The type-I sensors have more computation power, while the type-II sensors are low-end ones. The main objective of this paper is to design distributed optimal consensus filters for these two types of sensors, respectively, to estimate the state of the target based on the noisy measurements. Our derivation of the optimal filter is based on the use of minimum principle of Pontryagin (for type-I sensors) coupled with the Lagrange multiplier method and the results of generalized inverse of matrices (for type-II sensors). Simulation studies are presented to validate the performance of the proposed filters.

H/sub /spl infin// output feedback control of discrete-time fuzzy systems with application to chaos control

This paper presents an observer based H/sub /spl infin// output feedback synthesis method for discrete time fuzzy dynamic systems based on a piecewise Lyapunov function. The basic idea of the approach is to design an observer based piecewise linear output feedback control law to guarantee the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems. It is shown that the controller parameters can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible with commercially available software. Application to control chaotic systems is given to illustrate the effectiveness and advantages of the proposed method.

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.

Ubiquitous Monitoring for Industrial Cyber-Physical Systems Over Relay- Assisted Wireless Sensor Networks

Ubiquitous monitoring over wireless sensor networks (WSNs) is of increasing interest in industrial cyber-physical systems (CPSs). Question of how to understand a situation of physical system by estimating process parameters is largely unexplored. This paper is concerned with the distributed estimation problem for industrial automation over relay-assisted WSNs. Different from most existing works on WSN with homogeneous sensor nodes, the network considered in this paper consists of two types of nodes, i.e., sensing nodes (SNs), which is capable of sensing and computing, and relay nodes (RNs), which is only capable of simple data aggregation. We first adopt a Kalman filtering (KF) approach to estimate the unknown physical parameters. In order to facilitate the decentralized implementation of the KF algorithm in relay-assisted WSNs, a tree-based broadcasting strategy is provided for distributed sensor fusion. With the fused information, the consensus-based estimation algorithms are proposed for SNs and RNs, respectively. The proposed method is applied to estimate the slab temperature distribution in a hot rolling process monitoring system, which is a typical industrial CPS. It is demonstrated that the introduction of RNs improves temperature estimation efficiency and accuracy compared with the homogeneous WSN with SNs only.

Hierarchical-Game-Based Uplink Power Control in Femtocell Networks

Femtocells are viewed as a promising option for mobile operators to improve coverage and provide high-data-rate services in a cost-effective manner. However, cross-tier interference mitigation is considered to be one of the major challenges. Distributed-game-based power control approaches are effective for interference management in wireless networks. In this paper, we focus on the optimal power allocation for uplink transmission in two-tier femtocell networks and take into account the different service requirements and design objectives of macrocell user equipment (MUE) and femtocell user equipment (FUE) devices. The framework of hierarchical game with a multiple-leader-multiple-follower model is adopted to investigate the uplink power allocation problem. By using hierarchical game, on one hand, the utilities of both MUE and FUE devices are maximized; on the other hand, the uplink protection of MUE devices is highlighted. To obtain the game equilibrium (GE) distributively, we develop the iterative power update rules for both MUE and FUE devices. Moreover, given channel uncertainty, a robust hierarchical game is formulated and solved distributively. For implementations, the running process of the proposed hierarchical games is analyzed in detail. Finally, numerical results show the convergence of the hierarchical games without and with channel uncertainty. Moreover, it is also demonstrated that, in either case, the GE is unique and that the game is effective.

An Adaptive Deviation-tolerant Secure Scheme for distributed cooperative spectrum sensing

Distributed collaborative spectrum sensing is a promising method to improve the precision and efficiency of primary user detection in cognitive radio networks. Despite its performance advantages, it introduces new security issues that malicious or selfish nodes may manipulate false sensing data to degrade or even covert the sensing result of the whole network. Existing research often utilizes a threshold to distinguish honest users and malicious ones. However, determining such a threshold is difficult due to the dynamic characteristic of cognitive radio networks, and it is likely to misjudge an honest node with a relatively large deviation to be malicious. In this paper, we propose an Adaptive Deviation-tolerant Secure Scheme (ADS) for distributed collaborative spectrum sensing, which aims to mitigate the misbehaviors of inside malicious nodes and, at the same time, tolerant the large deviation introduced by honest users. ADS achieves the trade off of sensing security and deviation tolerance by assigning a dynamic weight to each sensing node and utilizes an adaptive threshold to minimize the negative effect on honest users. We evaluate the performance of the scheme through both analytical and simulation based study.

Backhaul-Aware User Association and Resource Allocation for Energy-Constrained HetNets

Growing attention has been paid to renewable- or hybrid-energy-powered heterogeneous networks (HetNets). In this paper, focusing on backhaul-aware joint user association and resource allocation for this type of HetNets, we formulate an online optimization problem to maximize the network utility reflecting proportional fairness. Since user association and resource allocation are tightly coupled not only on resource consumption of the base stations (BSs) but in the constraints of their available energy and backhaul as well, the closed-form solution is quite difficult to obtain. Thus, we solve the problem distributively by employing certain decomposition methods. Specifically, at first, by adopting the primal decomposition method, we decompose the original problem into a lower level resource-allocation problem for each BS and a higher level user-association problem. For the optimal resource allocation, we prove that a BS either assigns equal normalized resources or provides an equal long-term service rate to its served users. Then, the user-association problem is solved by the Lagrange dual decomposition method, and a completely distributed algorithm is developed. Moreover, applying results of the subgradient method, we demonstrate the convergence of the proposed distributed algorithm. Furthermore, to efficiently and reliably apply the proposed algorithm to the future wireless networks with an extremely dense BS deployment, we design a virtual user association and resource allocation scheme based on the software-defined networking architecture. Finally, numerical results validate the convergence of the proposed algorithm and the significant improvement on network utility, load balancing, and user fairness.