Robust control of uncertain time delay system:a novel sliding mode control design via LMI

The sliding mode control problem （SMC） is studied for a class of uncertain delay system in the presence of both parameter uncertainties and external disturbances. A novel virtual feedback control method is presented. Based on Lyapunov theory, sufficient conditions for design of the robust sliding mode plane are derived. Sliding mode controller based on reaching law concept is developed, which is to ensure system trajectories from any initial conditions asymptotically convergent to sliding mode plane. The global asymptotically stability of the closed-loop system is guaranteed. A numerical example with simulation results is given to illustrate the effectiveness of the methodology.

Study of nonlinear filter methods: particle filter

Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.

A weak condition for secant method to solve systems of nonlinear equations

In this paper, a new weak condition for the convergence of secant method to solve the systems of nonlinear equations is proposed. A convergence ball with the center x0 is replaced by that with xl, the first approximation generated by the secant method with the initial data x-1 and x0. Under the bounded conditions of the divided difference, a convergence theorem is obtained and two examples to illustrate the weakness of convergence conditions are provided. Moreover, the secant method is applied to a system of nonlinear equations to demonstrate the viability and effectiveness of the results in the paper.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

Routing Protocol for Heterogeneous FANETs with Mobility Prediction

In recent years,with the growth in Unmanned Aerial Vehicles(UAVs),UAV-based systems have become popular in both military and civil applications.In these scenarios,the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes,also known as Flying Ad Hoc Networks(FANETs).However,in FANETs,the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology,making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network(SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture,we apply an Extended Kalman Filter(EKF)for accurate mobility estimation and prediction of UAVs.In particular,we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard,we further propose a Directional Particle Swarming Optimization(DPSO)approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput,packet delivery ratio,and delay.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Processing Human Colonic Pressure Signals by Using Overdetermined ICA

独立部件分析(集成通信适配器)是为瞎的来源分离( BSS )的一个广泛地使用的方法成熟集成通信适配器模型有的 .The 来源的数字必须等于到传感器的限制过去常收集数据，它是难的在很实际的 cases.In 相遇这份报纸，一个 overdetermined 集成通信适配器方法在人的结肠的压力 signals.Using 主管部件分析( PCA )的分析被建议并且成功地使用了，方法第一估计来源的数字并且减少

Error Detecting Dual Basis Bit Parallel Systolic Multiplication Architecture over GF（2^m）

An error tolerant hardware efficient verylarge scale integration （VLSI） architecture for bitparallel systolic multiplication over dual base, which canbe pipelined, is presented. Since this architecture has thefeatures of regularity, modularity and unidirectionaldata flow, this structure is well suited to VLSIimplementations. The length of the largest delay pathand area of this architecture are less compared to the bitparallel systolic multiplication architectures reportedearlier. The architecture is implemented using Austria Micro System＇s 0.35 μm CMOS （complementary metaloxide semiconductor） technology. This architecture canalso operate over both the dual-base and polynomialbase.