In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.

This paper deals with the local state-feedback control design problems for continuous-time Takagi-Sugeno (T-S) fuzzy systems with the measurement errors in the state. The state-feedback controller is designed in such a way that the closed-loop system is locally asymptotically stable. The stability is guaranteed to be robust against the measurement errors in the state. In addition, an inner estimation of the domain of attraction (DA) is obtained as a sublevel set of the obtained quadratic Lyapunov function. The design procedure is formulated as optimizations subject to linear matrix inequalities (LMIs).

Successful image registration is an important step for object recognition, target detection, remote sensing, multimodal content fusion, scene blending, and disaster assessment and management. The geometric and photometric variations between images adversely affect the ability for an algorithm to estimate the transformation parameters that relate the two images. Local deformations, lighting conditions, object obstructions, and perspective differences all contribute to the challenges faced by traditional registration techniques. In this paper, a novel multistage registration approach is proposed that is resilient to view point differences, image content variations, and lighting conditions. Robust registration is realized through the utilization of a novel region descriptor which couples with the spatial and texture characteristics of invariant feature points. The proposed region descriptor is exploited in a multistage approach. A multistage process allows the utilization of the graph-based descriptor in many scenarios thus allowing the algorithm to be applied to a broader set of images. Each successive stage of the registration technique is evaluated through an effective similarity metric which determines subsequent action. The registration of aerial and street view images from pre- and post-disaster provide strong evidence that the proposed method estimates more accurate global transformation parameters than traditional feature-based methods. Experimental results show the robustness and accuracy of the proposed multistage image registration methodology.

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

This paper proposes an efficient way to compute the ℒ2-gains of discrete-time switched linear systems. Using the notion of generating functions, generalized versions of ℒ2-gains under arbitrary switching are studied. An efficient numerical algorithm is formulated by which these generalized ℒ2-gains can be estimated. The proposed method mitigates the problem of conservative bounds. Numerical examples are provided to illustrate the algorithm.

Successful content-based image registration relies on the accurate identification of corresponding features across images. Geometric and photometric transformations between images may hinder an algorithm's ability to precisely match features. In this work, we propose a novel region descriptor detection and matching algorithm for use with image registration. The detection process utilizes invariant feature points, as well as their spatial relationships and textural characteristics to create a connected graph whose structure represents an invariant region descriptor. With such a framework, feature matching can be accomplished by graph matching with a defined similarity metric. Subsequent image registration steps are outlined that employ the invariant region descriptors. The results provide strong evidence of the region descriptor's effectiveness in applications involving image registration. Several scenarios are presented including the registration of general objects, aerial photography, as well as scenes before and after a disaster.

This paper studies optimal control in a sensor network system consisting of mobile robots to minimize the overall energy consumption of the whole network. With communication energy cost and mobility energy cost taken into consideration, the problem is formulated as an optimal control of a hybrid system, which is solved by switched Linear Quadratic Regulator (LQR). Though switched LQR obtains globally optimal solution, the computational complexity is too high to implement the algorithm when the control horizon expands. For this reason, we resort to a switched system version of Receding Horizon Control (RHC), which is stable and provides a solution close to the optimal one. Finally, in order to attenuate the complexity due to the large networked system, the centralized RHC is modified into a distributed algorithm, which converges to a solution that can approximate the optimizer quite well as verified by simulations.

Exponential stability of switched linear systems under both arbitrary and proper switching is studied through two suitably defined families of functions called the strong and the weak generating functions. Various properties of the generating functions are established. It is found that the radii of convergence of the generating functions characterize the exponential growth rate of the trajectories of the switched linear systems. In particular, necessary and sufficient conditions for the exponential stability of the systems are derived based on these radii of convergence. Numerical algorithms for computing estimates of the generating functions are proposed and examples are presented for illustration purpose.

We present DRR-gossip, an energy-efficient and robust aggregate computation algorithm in sensor networks. We prove that the DRR-gossip algorithm requires O(n) messages and O(n3/2/log1/2 n) one-hop wireless transmissions to obtain aggregates on a random geometric graph. This reduces the energy consumption by at least a factor of 1/log n over the standard uniform gossip algorithm. Experiments validate the theoretical results and show that DRR-gossip needs much less transmissions than other gossip-based schemes.

Dynamical connection graph changes are inherent in networks such as peer-to-peer networks, wireless ad hoc networks, and wireless sensor networks. Considering the influence of the frequent graph changes is thus essential for precisely assessing the performance of applications and algorithms on such networks. In this paper, using stochastic hybrid systems (SHSs), we model the dynamics and analyze the performance of an epidemic-like algorithm, distributed random grouping (DRG), for average aggregate computation on a wireless sensor network with dynamical graph changes. Particularly, we derive the convergence criteria and the upper bounds on the running time of the DRG algorithm for a set of graphs that are individually disconnected but jointly connected in time. An effective technique for the computation of a key parameter in the derived bounds is also developed. Numerical results and an application extended from our analytical results to control the graph sequences are presented to exemplify our analysis.

In this paper, a method for reducing the power consumption in a pipeline of streaming data is presented. The pipeline is divided into multiple stages and buffer memories are inserted between adjacent stages. By dynamically changing the power state of each stage, the overall power consumption of the pipeline, including the power consumed by each stage and by each buffer, can be reduced. For example, when a buffer is full, the stage immediately before it can be turned off for a certain time period to save power. In this paper, the problem of finding the optimal strategy for managing the power state to minimize average power consumption is studied for a pipeline of data consisting of three stages with two buffers in between. The problem is formulated as an optimal control problem of a hybrid system, namely, a dynamical system with both continuous dynamics and discrete transitions. Several important properties of the optimal solutions are derived. Using these properties, the optimal periodic solutions are obtained analytically for the special case when each stage is allowed to turn on and off at most once during each period. Simulation results using practical data are presented. The results show substantial power savings of the proposed method over heuristic buffering strategies

In this paper, the problem of automated aircraft conflict prediction is studied for two-aircraft midair encounters. A model is introduced to predict the aircraft positions along some look-ahead time horizon, during which each aircraft is trying to follow a prescribed flight plan despite the presence of additive wind perturbations to its velocity. A spatial correlation structure is assumed for the wind perturbations such that the closer the two aircraft, the stronger the correlation between the perturbations to their velocities. Using this model, a method is introduced to evaluate the criticality of the encounter situation by estimating the probability of conflict, namely, the probability that the two aircraft come closer than a minimum allowed distance at some time instant during the look-ahead time horizon. The proposed method is based on the introduction of a Markov chain approximation of the stochastic processes modeling the aircraft motions. Several generalizations of the proposed approach are also discussed.

Conflict detection and resolution schemes operating at the mid-range and short-range level of the air traffic management process are discussed. Probabilistic models for predicting the aircraft position in the near-term and mid-term future are developed. Based on the mid-term prediction model, the maximum instantaneous probability of conflict is proposed as a criticality measure for two aircraft encounters. Randomized algorithms are introduced to efficiently estimate this measure of criticality and provide quantitative bounds on the level of approximation introduced. For short-term detection, approximate closed-form analytical expressions for the probability of conflict are obtained, using the short-term prediction model. Based on these expressions, an algorithm for decentralized conflict detection and resolution that generalizes potential fields methods for path planning to a probabilistic dynamic environment is proposed. The algorithms are validated using Monte Carlo simulations.