Unmanned aerial vehicle (UAV) resource scheduling means to allocate and aggregate the available UAV resources depending on the mission requirements and the battlefield situation assessment. In previous studies, the models cannot reflect the mission synchronization; the targets are treated respectively, which results in the large scale of the problem and high computational complexity. To overcome these disadvantages, a model for UAV resource scheduling under mission synchronization is proposed, which is based on single-objective non-linear integer programming. And several cooperative teams are aggregated for the target clusters from the available resources. The evaluation indices of weapon allocation are referenced in establishing the objective function and the constraints for the issue. The scales of the target clusters are considered as the constraints for the scales of the cooperative teams to make them match in scale. The functions of the intersection between the “mission time-window” and the UAV “arrival time-window” are introduced into the objective function and the constraints in order to describe the mission synchronization effectively. The results demonstrate that the proposed expanded model can meet the requirement of mission synchronization, guide the aggregation of cooperative teams for the target clusters and control the scale of the problem effectively.
Active fault-tolerant control is investigated for a class of uncertain SISO nonlinear flight control systems based on the adaptive observer, feedback linearization and backstepping theory. Firstly an adaptive observer is constructed to estimate the fault in the faulty system. A new fault updating law is presented to simplify the assumption conditions of the adaptive observer. The asymptotical stability of the observer and the uniform ultimate boundedness of the fault estimation error are guaranteed by Lyapunov theorem. Then a backstepping-based active fault-tolerant controller is designed for the faulty system. The asymptotical stability of the closed-loop system and uniform ultimate boundedness of the tracking error are proved based on Lyapunov theorem. The effectiveness of the proposed scheme is demonstrated through the numerical simulation of a flight control system.
A novel heuristic search algorithm called seeker op- timization algorithm(SOA)is proposed for the real-parameter optimization.The proposed SOA is based on simulating the act of human searching.In the SOA,search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule.The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution(DE)and three modified particle swarm optimization(PSO)algorithms.The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
An improved genetic algorithm (IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed. Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value, the degree of constraints violations and the number of constraints violations. It is easy to distinguish excellent individuals from general individuals by using an individuals' feature vector. Additionally, a local search (LS) process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions. The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions. Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics, and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness. Finally, the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image, and the final small target is worked out by a simple partitioning algorithm. The test results in various space-based downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate. It is a kind of infrared small and dim target detection method with good performance.
In supply chain management, an important research direction is to coordinate the supply chain through introducing flexible contracts. A supply chain contract is flexible if it can satisfy two conditions at the same time: the supply chain is coordinated, and the total profit of the supply chain can be arbitrarily divided between the supply chain members. This paper puts out two contracts, a flexible return contract and a flexible wholesale price discount contract. In contrast to many of literature, the supply chain contracts with an endogenous wholesale price is specifically considered, and a detailed sensitivity analysis of the contract parameters is given. The paper also discusses the application of the contract in vendor-managed inventory (VMI) mode. The results show that the supply chain’s performance is improved after introducing above contracts. All the findings are illustrated by numerical examples.
This paper is concerned with the H∞ fault detection for continuous-time linear switched systems with its application to turntable systems. The solvability condition for a desired filter is established based on the proposed sufficient condition. Based on the double channel scheme of the turntable control system, the turntable system can be modeled as a switched system. Finally, by taking the turntable system as a numerical example, the effectiveness of the proposed theory is well validated.
A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems (CBIR). The color feature is described by the color histogram (CH), which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform (L-DT) is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.
Aiming at the problem of detecting a distributed target when signal mismatch occurs, this paper proposes a tunable detector parameterized by an adjustable parameter. By adjusting the parameter, the tunable detector can achieve robust or selective detection of mismatched signals. Moreover, the proposed tunable detector, with a proper tunable parameter, can provide higher detection probability compared with existing detectors in the case of no signal mismatch. In addition, the proposed tunable detector possesses the constant false alarm rate property with the unknown noise covariance matrix.
In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.
Evolutionary algorithms (EAs) have been used in high utility itemset mining (HUIM) to address the problem of discovering high utility itemsets (HUIs) in the exponential search space. EAs have good running and mining performance, but they still require huge computational resource and may miss many HUIs. Due to the good combination of EA and graphics processing unit (GPU), we propose a parallel genetic algorithm (GA) based on the platform of GPU for mining HUIM (PHUI-GA). The evolution steps with improvements are performed in central processing unit (CPU) and the CPU intensive steps are sent to GPU to evaluate with multi-threaded processors. Experiments show that the mining performance of PHUI-GA outperforms the existing EAs. When mining 90% HUIs, the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.
Due to people’s increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts’ preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts ’ importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
A novel identification method for point source, coherently distributed (CD) source and incoherently distributed (ICD) source is proposed. The differences among the point source, CD source and ICD source are studied. According to the different characters of covariance matrix and general steering vector of the array received source, a second order blind identification method is used to separate the sources, the mixing matrix could be obtained. From the mixing matrix, the type of the source is identified by using an amplitude criterion. And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix. omputer simulations validate the efficiency of the method.
The application of data envelopment analysis (DEA) as a multiple criteria decision making (MCDM) technique has been gaining more and more attention in recent research. In the practice of applying DEA approach, the appearance of uncertainties on input and output data of decision making unit (DMU) might make the nominal solution infeasible and lead to the efficiency scores meaningless from practical view. This paper analyzes the impact of data uncertainty on the evaluation results of DEA, and proposes several robust DEA models based on the adaptation of recently developed robust optimization approaches, which would be immune against input and output data uncertainties. The robust DEA models developed are based on input-oriented and outputoriented CCR model, respectively, when the uncertainties appear in output data and input data separately. Furthermore, the robust DEA models could deal with random symmetric uncertainty and unknown-but-bounded uncertainty, in both of which the distributions of the random data entries are permitted to be unknown. The robust DEA models are implemented in a numerical example and the efficiency scores and rankings of these models are compared. The results indicate that the robust DEA approach could be a more reliable method for efficiency evaluation and ranking in MCDM problems.
A new fault tolerant control (FTC) via a controller reconfiguration approach for general stochastic nonlinear systems is studied. Different from the formulation of classical FTC methods, it is supposed that the measured information for the FTC is the probability density functions (PDFs) of the system output rather than its measured value. A radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. As a result, the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.
To maintain the stability of the inter-satellite link for gravitational wave detection, an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed. Different from the traditional fault diagnosis optimization algorithms, the fault intelligent learning method proposed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong coupling nonlinearity. By constructing a two-layer learning network, the method enables efficient joint diagnosis of fault areas and fault parameters. The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s, and the fault diagnosis efficiency is improved by 99.8% compared with the traditional algorithm.
With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess, the cloud control system (CCS) has exhibited formidable potential in the realm of connected assisted driving, such as the adaptive cruise control (ACC). Based on the CCS architecture, this paper proposes a cloud-based predictive ACC (PACC) strategy, which fully considers the road slope information and the preceding vehicle status. In the cloud, based on the dynamic programming (DP), the long-term economic speed planning is carried out by using the slope information. At the vehicle side, the real-time fusion planning of the economic speed and the preceding vehicle state is realized based on the model predictive control (MPC), taking into account the safety and economy of driving. In order to ensure the safety and stability of the vehicle-cloud cooperative control system, an event-triggered cruise mode switching method is proposed based on the state of each subsystem of the vehicle-cloud-network-map. Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions. Moreover, under normal conditions, compared to the ACC system, the PACC system can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle, thus achieving fuel savings of 3% to 8%.
An adaptive robust approach for actuator fault-tolerant control of a class of uncertain nonlinear systems is proposed. The two chief ways in which the system performance can degrade following an actuator-fault are undesirable transients and unacceptably large steady-state tracking errors. Adaptive control based schemes can achieve good final tracking accuracy in spite of change in system parameters following an actuator fault, and robust control based designs can achieve guaranteed transient response. However, neither adaptive control nor robust control based fault-tolerant designs can address both the issues associated with actuator faults. In the present work, an adaptive robust fault-tolerant control scheme is claimed to solve both the problems, as it seamlessly integrates adaptive and robust control design techniques. Comparative simulation studies are performed using a nonlinear hypersonic aircraft model to show the effectiveness of the proposed scheme over a robust adaptive control based faulttolerant scheme.
A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are extracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondly, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object windows. Lastly, the final object windows are got from initial candidate windows based on local and spatial structure feature matching. Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.
This article deals with the uniformly globally asymptotic controllability of discrete nonlinear systems with disturbances. It is shown that the system is uniformly globally asymptotic controllability with respect to a closed set if and only if there exists a smooth control Lyapunov function. Further, it is obtained that the control Lyapunov function may be used to construct a feedback law to stabilize the closed-loop system. In addition, it is proved that for periodic discrete systems, the resulted control Lyapunov functions are also time periodic.
The aim of this paper is to develop controllers for uncertain systems in the presence of stuck type actuator failures. A new scheme is proposed to design output feedback controllers for a class of uncertain systems having redundant control inputs, with which the relative degrees of transfer functions are different. To deal with these inputs using backstepping technique, a pre-filter is introduced before each actuator such that its output is the input to the actuator. The orders of the pre-filters are chosen properly to ensure all their inputs can be designed at the same step in the systematic design. To compensate for the effects of possible failed actuators, more uncertain parameters than system parameters are required to be identified. With the proposed scheme, the global boundedness of the closed-loop system can still be ensured and the system output can be regulated to a specific value when some of the actuators' outputs are stuck at unknown fixed values.
As one of the next generation imaging spectrometers, the interferential spectrometer (IS) possesses the advantages of high throughput, multi-channel and great resolution. The data of IS are produced in the spatial domain, but optical applications are in the Fourier domain. Traditional compression methods can only protect the visual quality of interferometer data in the spatial domain but ignore the distortion in the Fourier domain. The relation between the distortion in the Fourier domain and the compression in the spatial domain is analyzed. By mathematical proof and validation with experiments, the relation between spatial and Fourier domains is discovered, and the significance in the Fourier domain is more important as optical path difference (OPD) increasing in the spatial domain. Based on this relation, a novel coding scheme is proposed, which can compress data in the spatial domain while reducing the distortion in the Fourier domain. In this scheme, the bit stream of the set partitioning in hierarchical trees (SPIHT) is truncated by adaptively lifting rate-distortion slopes according to the priorities of OPD based on rate-distortion optimization theory. Experimental results show that the proposed method can provide better protection of spectrum curves in the Fourier domain while maintaining a comparable visual quality in the spatial domain.
To improve the total throughput of the uplink orthogonal frequency division multiple access system, a low complexity hybrid power distribution (HPD) combined with subcarrier allocation scheme is proposed. For the fairness mechanism for the subcarrier, the inter-cell interference is first analyzed to calculate the capacity of the multi-cell. The user selects the subcarrier with the largest channel gain. Based on the above subcarrier allocation scheme, a new kind of HPD scheme is proposed, which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter, respectively. Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell, the proposed HPD scheme decreases the system complexity significantly, meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.
A novel class of periodically changing features hidden in radar pulse sequence environment, named G features, is proposed. Combining fractal theory and Hilbert-Huang transform, the features are extracted using changing characteristics of pulse parameters in radar emitter signals. The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap. Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features, therefore provide a new methodology for signal sorting.
The proposed Doppler measurement technique shows that the Doppler measurements can be accomplished by a single pulse with multiple frequency components through optical fibre delay lines. Range and velocity ambiguity can be removed, and the velocity resolution can be improved dramatically by using long optical fibre delay lines. Furthermore, the velocity resolution can be modified by adjusting the length of optical fibre delay lines. In addition, the proposed radar can achieve high range resolution by using a single wideband pulse. As a result, the new approach can improve radar performance significantly.
The fuzzy non-cooperative game with fuzzy payoff function is studied. Based on fuzzy set theory with game theory, the fuzzy Nash equilibrium of fuzzy non-cooperative games is proposed. Most of researchers rank fuzzy number by its center of gravity or by the real number with its maximal membership. By reducing fuzzy number into a real number, we lose much fuzzy information that should be kept during the operations between fuzzy numbers. The fuzzy quantities or alternatives are ordered directly by Yuan’s binary fuzzy ordering relation. In doing so, the existence of fuzzy Nash equilibrium for fuzzy non-cooperative games is shown based on the utility function and the crisp Nash theorem. Finally, an illustrative example in traffic flow patterns of equilibrium is given in order to show the detailed calculation process of fuzzy Nash equilibrium.
A new non-decoupling three-dimensional guidance law is proposed for bank-to-turn (BTT) missiles with the motion coupling problem. In this method, the different geometry is taken for theoretically modeling on BTT missiles’ motion within the threedimensional style without information loss, and meanwhile, Liegroup is utilized to describe the line-of-sight (LOS) azimuth when the terminal angular constraints are considered. Under these circumstances, a guidance kinematics model is established based on differential geometry. Then, corresponding to no terminal angular constraint and terminal angular constraints, guidance laws are respectively designed by using proportional control and generalized proportional-derivative (PD) control in SO(3) group. Eventually, simulation results validate that this developed method can effectively avoid the complexity of pure Lie-group method and the information loss of the traditional decoupling method as well.
As a dynamic projection to latent structures (PLS) method with a good output prediction ability, dynamic inner PLS (DiPLS) is widely used in the prediction of key performance indicators. However, due to the oblique decomposition of the input space by DiPLS, there are false alarms in the actual industrial process during fault detection. To address the above problems, a dynamic modeling method based on autoregressive-dynamic inner total PLS (AR-DiTPLS) is proposed. The method first uses the regression relation matrix to decompose the input space orthogonally, which reduces useless information for the prediction output in the quality-related dynamic subspace. Then, a vector autoregressive model (VAR) is constructed for the prediction score to separate dynamic information and static information. Based on the VAR model, appropriate statistical indicators are further constructed for online monitoring, which reduces the occurrence of false alarms. The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
Autonomous umanned aerial vehicle (UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decision-making policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods. Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes (MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.
The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR (inverse synthetic aperture radar), which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM (frequency modulation) parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT (Radon-Wigner transform). The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.
This paper is concerned with the robust stabilization problem of networked control systems with stochastic packet dropouts and uncertain parameters. Considering the stochastic packet dropout occuring in two channels between the sensor and the controller, and between the controller and the actuator, networked control systems are modeled as the Markovian jump linear system with four operation modes. Based on this model, the necessary and sufficient conditions for the mean square stability of the deterministic networked control systems and uncertain networked control systems are given by using the theory of the Markovian jump linear system, and corresponding controller design procedures are proposed via the cone complementarity linearization method. Finally, the numerical example and simulations are given to illustrate the effectiveness of the proposed results.
This paper considers the problem of reference tracking control for the flexible air-breathing hypersonic flight vehicle with actuator delay and uncertainty. By constructing the Lyapunov functional including the lower and upper bounds of the time-varying delay, the non-fragile controller is designed such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed performance cost index. The simulation results are given to show the effectiveness of the proposed control method, which is validated by excellent output reference altitude and velocity tracking performance.
An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control (CFTOC) problem for discrete-time piecewise affine (PWA) systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.
For the estimation of MIMO frequency selective channel, to mitigate the curse of dimensionality, a novel particle filtering scheme combined with time delay domain processing is proposed. In order to extract the time delay domain channel impulse response from the observed signal, the least-squares (LS) and minimum mean squared error (MMSE) criteria are discussed and the comparable performance of LS with MMSE for samplespaced channel is revealed. Incorporated the dynamical channel model, gradient particle filtering is further introduced to improve the estimation performance. The robustness of the channel estimator for underestimated Doppler frequency and the effectiveness of the new estimation scheme are illustrated through simulation at last.
The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances. Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturbance modeled by an exo-system and norm bounded uncertain disturbance. A nonlinear disturbance observer is designed to estimate the modeled disturbance. Then, the fault diagnosis observer is constructed by integrating disturbance observer with disturbance attenuation and rejection performances. The augmented Lyapunov functional approach, which involves the tuning parameter and slack variable, is applied to make the solution of inequality more flexible. Finally, applications for a two-link robotic manipulator system are given to show the efficiency of the proposed approach.