The problem of robust H∞ guaranteed cost satisfactory fault-tolerant control with quadratic D stabilizability against actuator failures is investigated for a class of discrete-time systems with value-bounded uncertainties existing in both the state and control input matrices. Based on a more practical and general model of actuator continuous gain failures, taking the transient property, robust behaviour on H∞ performance and quadratic cost performance requirements into consideration, sufficient conditions for the existence of satisfactory fault-tolerant controller are given and the effective design steps with constraints of multiple performance indices are provided. Meanwhile, the consistency of the regional pole index, H∞ norm-bound constraint and cost performance indices is set up for fault-tolerant control. A simulation example shows the effectiveness of the proposed method.
A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition. Firstly, the centroid distance and azimuth angle of each boundary point are computed. Then, with a prior-defined angle interval, all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise. After that, the centroid distance ratios (CDRs) of any two opposite contour points to the barycenter are achieved as the representation of the shape, which will be invariant to affine transformation. Since the angles of contour points will change non-linearly among affine related images, the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding points. The core issue is how to determine the angle positions for sampling, which can be regarded as an optimization problem of path planning. An ant colony optimization (ACO)-based path planning model with some constraints is presented to address this problem. Finally, the Euclidean distance is adopted to evaluate the similarity of shape features in different images. The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation, scaling, rotation and distortion.
The group decision making problem with linguistic pref- erence relations is studied.The concept of additive consistent linguistic preference relation is defined,and then some properties of the additive consistent linguistic preference relation are studied. In order to rank the alternatives in the group decision making with the linguistic preference relations,the weighted average is first utilized to combine the group linguistic preference relations to one linguistic preference relation,and then the transformation function is proposed to transform the linguistic preference relation to the multiplicative preference relation,and thus the Saaty’s eigenvec- tor method(EM)of multiplicative preference relation is utilized to rank the alternatives in group decision making with the linguistic preference relations.Finally,an illustrative numerical example is given to verify the proposed method.A comparative study to the linguistic ordered weighted averaging(LOWA)operator method is also demonstrated.
This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model (HMM) parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence (EPS) which consists of a series of events is computed to describe the unique characteristic of human activities. The analysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.
Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial (NP)-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm (QSFLAR) is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representative algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.
The aim of this paper is to discuss the approximate rea- soning problems with interval-valued fuzzy environments based on the fully implicational idea.First,this paper constructs a class of interval-valued fuzzy implications by means of a type of impli- cations and a parameter on the unit interval,then uses them to establish fully implicational reasoning methods for interval-valued fuzzy modus ponens(IFMP)and interval-valued fuzzy modus tol- lens(IFMT)problems.At the same time the reversibility properties of these methods are analyzed and the reversible conditions are given.It is shown that the existing unified forms ofα-triple I(the abbreviation of triple implications)methods for FMP and FMT can be seen as the particular cases of our methods for IFMP and IFMT.
Space electromagnetic docking technology, free of propellant and plume contamination, offers continuous, reversible and synchronous controllability, which is widely applied in the future routine on-orbit servicing missions. Due to the inherent nonlinearities, couplings and uncertainties of an electromagnetic force model, the dynamics and control problems of them are difficult. A new modeling approach for relative motion dynamics with intersatellite force is proposed. To resolve these control problems better, a novel nonlinear control method for soft space electromagnetic docking is proposed, which combines merits of artificial potential function method, Lyapunov theory and extended state observer. In addition, the angular momentum management problem of space electromagnetic docking and approaches of handling it by exploiting the Earth’s magnetic torque are investigated. Finally, nonlinear simulation results demonstrate the feasibility of the dynamic model and the novel nonlinear control method.
In order to avoid staircasing effect and preserve small scale texture information for the classical total variation regularization, a new minimization energy functional model for image decomposition is proposed. Firstly, an adaptive regularization based on the local feature of images is introduced to substitute total variational regularization. The oscillatory component containing texture and/or noise is modeled in generalized function space div (BMO). And then, the existence and uniqueness of the minimizer for proposed model are proved. Finally, the gradient descent flow of the Euler-Lagrange equations for the new model is numerically implemented by using a finite difference method. Experiments show that the proposed model is very robust to noise, and the staircasing effect is avoided efficiently, while edges and textures are well remained.
A new method for array calibration of array gain and phase uncertainties, which severely degrade the performance of spatial spectrum estimation, is presented. The method is based on the idea of the instrumental sensors method (ISM), two well-calibrated sensors are added into the original array. By applying the principle of estimation of signal parameters via rotational invariance techniques (ESPRIT), the direction-of-arrivals (DOAs)and uncertainties can be estimated simultaneously through eigen-decomposition. Compared with the conventional ones, this new method has less computational complexity while has higher estimation precision, what’s more, it can overcome the problem of ambiguity. Both theoretical analysis and computer simulations show the effectiveness of the proposed method.
The robust reliable H∞ control problem for discrete-time Markovian jump systems with actuator failures is studied. A more practical model of actuator failures than outage is considered. Based on the state feedback method, the resulting closed-loop systems are reliable in that they remain robust stochastically stable and satisfy a certain level of H∞ disturbance attenuation not only when all actuators are operational, but also in case of some actuator failures. The solvability condition of controllers can be equivalent to a feasibility problem of coupled linear matrix inequalities (LMIs). A numerical example is also given to illustrate the design procedures and their effectiveness.
The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems (CSoS), which can be abstracted as a heterogeneous combat network (HCN). It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS. To this end, this paper proposes an integrated framework called HCN disintegration based on double deep $Q$-learning (HCN-DDQL). Firstly, the enemy’s CSoS is abstracted as an HCN, and an evaluation index based on the capability and attack costs of nodes is proposed. Meanwhile, a mathematical optimization model for HCN disintegration is established. Secondly, the learning environment and double deep $Q$-network model of HCN-DDQL are established to train the HCN’s disintegration strategy. Then, based on the learned HCN-DDQL model, an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed. Finally, a case study is used to demonstrate the reliability and effectiveness of HCN-DDQL, and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.
The mixture of factor analyzers(MFA)can accurately describe high resolution range profile(HRRP)statistical charac- teristics.But how to determine the proper number of the models is a problem.This paper develops a variational Bayesian mixture of factor analyzers(VBMFA)model.This procedure can obtain a lower bound on the Bayesian integral using the Jensen’s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model au- tomatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
Most of the reconstruction-based robust adaptive beamforming (RAB) algorithms require the covariance matrix reconstruction (CMR) by high-complexity integral computation. A Gauss-Legendre quadrature (GLQ) method with the highest algebraic precision in the interpolation-type quadrature is proposed to reduce the complexity. The interference angular sector in RAB is regarded as the GLQ integral range, and the zeros of the three-order Legendre orthogonal polynomial is selected as the GLQ nodes. Consequently, the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral. The new method has significantly reduced the complexity as compared to most state-of-the-art reconstruction-based RAB techniques, and it is able to provide the similar performance close to the optimal. These advantages are verified by numerical simulations.
According to the characteristic of cruise missiles, navigation point setting is simplified, and the principle of route planning for saturation attack and a concept of reference route are put forward. With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets, a reference route algorithm is built on the shortest range and threat avoidance. Then a route-flight-time algorithm is built on navigation points. Based on the conditions of multi-direction saturation attack, a route planning algorithm of multi-direction saturation attack is built on reference route, route-flight-time, and impact azimuth. Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.
The track association problem of radar and electronic support measure (ESM) has been considered in the literature for several years. This problem is crucial for radar-to-ESM track fusion and is complicated by the presence of individual systematic errors and measurement errors. In order to improve the track association of radar and ESM sensors, a pseudo-linear filtering algorithm is proposed to estimate the target states and improve the stability of the filter. It is found that, however, the correct probability of radarto-ESM track association decreases as the radar measurement error decreases, when the pseudo-linear filter is used for ESM sensor filtering. In view of the strange phenomenon, this paper analyzes the reason for it by using the statistic theory and further performs Monte Carlo simulation to verify the analysis.
The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.
A new recursive algorithm with the partial parallel structure based on the linearly constrained minimum variance (LCMV) criterion for adaptive monopulse systems is proposed. The weight vector associated with the original whole antenna array is decomposed into several adaptive weight sub-vectors firstly. An adaptive algorithm based on the conventional LCMV principle is then deduced to update the weight sub-vectors for sum and difference beam, respectively. The optimal weight vector can be obtained after convergence. The required computational complexity is evaluated for the proposed technique, which is on the order of O(N) and less than that of the conventional LCMV method. The flow chart scheme with the partial parallel structure of the proposed algorithm is introduced. This scheme is easy to be implemented on a distributed computer/digital signal processor (DSP) system to solve the problems of the heavy computational burden and vast data transmission of the large-scale adaptive monopulse array. Then, the monopulse ratio and convergence rate of the proposed algorithm are evaluated by numerical simulations. Compared with some recent adaptive monopulse estimation methods, a better performance on computational complexity and monopulse ratio can be achieved with the proposed adaptive method.
For decreasing the multiple access interference of weaker signal acquisition in direct sequence spread spectrum (DSSS) systems, a new single decision algorithm is presented. The maximum value of correlation results is conventionally detected. However, there may be not only one strong peak among correlation results when the cross-correlation noise is strong enough to affect the correlation results. The proposed algorithm decreases the false alarm probability through the decision of the ratio of the maximum value and the second maximum value of the correlation results. Theoretical analysis and simulation results indicate that the proposed algorithm effectively suppresses the acquisition problem of multiple access interference in DSSS system.
To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture (BLS-GSM).
To design approximately linear-phase complex coefficient finite impulse response (FIR) digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter’s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.
Event region detection is the important application for wireless sensor networks (WSNs), where the existing faulty sensors would lead to drastic deterioration of network quality of service. Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance (DFWD) is proposed, which exploits the spatial correlation among sensor nodes and their redundant information. In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node. Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node. Simultaneously, readings of faulty sensors would be corrected during this process. Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.
A phase-domain blind estimator of symbol duration based on Haar wavelet transform (HWT) is proposed. It can estimate the symbol duration of phase modulated signals, such as M-ary phase-shift keying (MPSK) signals and polyphase coded signals. The closed form of the spectrum of HWT is derived. Theoretical analysis shows the frequency of the first spectral peak is equal to the symbol rate, which is the reciprocal of symbol duration. Thus the symbol duration can be extracted from the spectrum. Subsequently, the optimum wavelet scale is determined according to the maximum output signal to noise ratio (OSNR) criterion. MATLAB simulations show that this algorithm can blindly estimate the symbol duration without any prior knowledge. This estimator need not estimate the carrier frequency and has the characteristics of low computation complexity and high accuracy.
In some object tracking systems, the moving object future position is an area (i.e., target area). It is a successful estimation strategy if the predicted points fall in the target area. If the object makes a sudden maneuvering, the prediction may get out of the target area easily which may make the tracking system lose the object. The aim is to investigate the admissible maximum object maneuvering intensity, which is characterized as model noise variance, for such kind of tracking system. Firstly, the concept of stochastic passage characteristics over the boundary of target area and their relationship with prediction error variance are described. Secondly, the consistency among the indices of regional pole, prediction error variance and stochastic passage characteristics is analyzed. Thirdly, the multi-indices constraints are characterized by a set of bi-linear matrix inequalities (BMIs). Then, the admissible maximum model noise variance and the satisfactory estimation strategy are presented by iteratively solving linear matrix inequalities (LMIs) to approximate BMIs. Finally, a numerical example is proposed to demonstrate the obtained results.
An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems. Both the designed observer and controller are free from time delays. Different from the existing results, this paper need not the assumption that the upper bounding functions of time-delay terms are known, and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms, so the designed controller procedure is more simplified. In addition, the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded, and the output regulation error converges to a small residual set around the origin. Two simulation examples are provided to verify the effectiveness of control scheme.
Cooperative communication can achieve spatial diversity gains, and consequently combats signal fading due to multipath propagation in wireless networks powerfully. A novel complex field network-coded cooperation (CFNCC) scheme based on multi-user detection for the multiple unicast transmission is proposed. Theoretic analysis and simulation results demonstrate that, compared with the conventional cooperation (CC) scheme and network-coded cooperation (NCC) scheme, CFNCC would obtain higher network throughput and consumes less time slots. Moreover, a further investigation is made for the symbol error probability (SEP) performance of CFNCC scheme, and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio (SNR) values.
This paper proposes an adaptive augmentation control design approach of the gain-scheduled controller. This extension is motivated by the need for augmentation of the baseline gainscheduled controller. The proposed approach can be utilized to design flight control systems for advanced aerospace vehicles with a large parameter variation. The flight dynamics within the flight envelope is described by a switched nonlinear system, which is essentially a switched polytopic system with uncertainties. The flight control system consists of a baseline gain-scheduled controller and a model reference adaptive augmentation controller, while the latter can recover the nominal performance of the gainscheduled controlled system under large uncertainties. By the multiple Lyapunov functions method, it is proved that the switched nonlinear system is uniformly ultimately bounded. To validate the effectiveness of the proposed approach, this approach is applied to a generic hypersonic vehicle, and the simulation results show that the system output tracks the command signal well even when large uncertainties exist.
The problem on stabilization for the system with distributed delays is researched. The distributed time-delay under consideration is assumed to be a constant time-delay, but not known exactly. A design method is proposed for a memory proportional and integral (PI) feedback controller with adaptation to distributed time-delay. The feedback controller with memory simultaneously contains the current state and the past distributed information of the addressed systems. The design for adaptation law to distributed delay is very concise. The controller can be derived by solving a set of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness of the design method.
The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.
Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in “pop-up” targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval (CPI) must be long enough to achieve a certain signal-to-noise ratio (SNR) that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as −26 dB in the experimental system can possibly determine the CPI.
To solve the multi-class fault diagnosis tasks, decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with “one-against-all” and “one-against-one” demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.
Frequency-invariant beamformer (FIB) design is a key issue in wideband array signal processing. To use commonly wideband linear array with tapped delay line (TDL) structure and complex weights, the FIB design is provided according to the rule of minimizing the sidelobe level of the beampattern at the reference frequency while keeping the distortionless response constraint in the mainlobe direction at the reference frequency, the norm constraint of the weight vector and the amplitude constraint of the averaged spatial response variation (SRV). This kind of beamformer design problem can be solved with the interior-point method after being converted to the form of standard second order cone programming (SOCP). The computer simulations are presented which illustrate the effectiveness of our FIB design method for the wideband linear array with TDL structure and complex weights.
The class of multiple attribute decision making (MADM) problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.
A new procedure for a design of multi-range controllers for use with highly nonlinear systems is developed. The procedure involves obtaining the describing function models of the nonlinear plant by software followed by designing a controller at nominal conditions. Then, the controller parameters are optimized to yield a satisfactory closed-loop response at all operating regimes. Finally, the performance and stability of the closed-loop system comprised of the designed controller and the nonlinear plant are verified. The procedure and the associated software are applied to a nonlinear control problem of the sort encountered in aerospace, and the results are compared with two other approaches.
Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.
