To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model (FOM), a modular FOM is proposed by high level architecture (HLA) evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model (BOM) is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.
For infrared focal plane array sensors, imagery is degraded during signal acquisition, particularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readoutchannel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.
A methodology for automatically generating risk scenarios is presented. Its main idea is to let the system model “express itself” through simulation. This is achieved by having the simulation model driven by an elaborated simulation engine, which: (i) manipulates the generation of branch points, i.e. event occurrence times; (ii) employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point. In addition, a backtracking technique, as an extension, is implemented to recover some missed risk scenarios. A widely discussed dynamic reliability example (a holdup tank) is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.
Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers. This article presents a scheme of adopting linear parameter-varying (LPV) control approach with full block multipliers to design a missile robust gain scheduling autopilot in order to eliminate conservatism. A model matching design structure with a high demand on matching precision is constructed based on the missile linear fractional transformation (LFT) model. By applying full block S-procedure and elimination lemma, a convex feasibility problem with an infinite number of constraints is formulated to satisfy robust quadratic performance specifications. Then a grid method is adopted to transform the infinite-dimensional convex feasibility problem into a solvable finite-dimensional convex feasibility problem, based on which a gain scheduling controller with linear fractional dependence on the flight Mach number and altitude is derived. Static and dynamic simulation results show the effectiveness and feasibility of the proposed scheme.
Due to defects of time-difference of arrival localization, which influences by speed differences of various model waveforms and waveform distortion in transmitting process, a neural network technique is introduced to calculate localization of the acoustic emission source. However, in back propagation (BP) neural network, the BP algorithm is a stochastic gradient algorithm virtually, the network may get into local minimum and the result of network training is dissatisfactory. It is a kind of genetic algorithms with the form of quantum chromosomes, the random observation which simulates the quantum collapse can bring diverse individuals, and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity. Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy, so it has a good application prospect and is worth researching further more.
The problem of designing fuzzy static output feedback controller for T-S discrete-time fuzzy bilinear system(DFBS)is presented.Based on parallel distribution compensation method, some sufficient conditions are derived to guarantee the stability of the overall fuzzy system.The stabilization conditions are further formulated into linear matrix inequality(LMI)so that the desired controller can be easily obtained by using the Matlab LMI toolbox. In comparison with the existing results,the drawbacks,such as coordinate transformation,same output matrices,have been elim- inated.Finally,a simulation example shows that the approach is effective.
The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with. The proposed approach is applied in two steps: first, the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method, and second, the unambiguous slant-range velocity is found using a track-based criterion. The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large. Besides, the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal, therefore, the location performance would not be sacrificed in order to suppress clutter and/or interference. The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.
A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic generalized ordered weighted averaging (LGOWA) operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average (WA) and in the ordered weighted averaging (OWA) function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic OWA (LOWA) operator and the linguistic ordered weighted quadratic averaging (LOWQA) operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.
High complexity and high latency are key problems for multiuser detection (MUD) to be applied to a mobile station in cellular networks. To tackle these problems, an interleave division multiple access (IDMA) based multiple access scheme, grouped spread IDMA (GSIDMA), is proposed. In a GSIDMA system, lower complexity and latency for mobile stations can be achieved by appropriately dividing active users into different groups. The system model of GSIDMA is constructed and followed by analysing on its system capacity, complexity and latency, and bit error rate (BER) performance. The extrinsic information transfer (EXIT) chart is used to analyze the convergence behavior of the iteration process. The grouping method and interleavers-reuse issue for GSIDMA are also discussed preliminarily. The analyses and simulation results indicate that the complexity and latency of the proposed scheme are much lower than those of IDMA, whereas its BER performance is close to the latter. The properties of low complexity and low latency make it more feasible for the practical implementation.
The problem of the quantized dynamic output feedback controller design for networked control systems is mainly discussed. By using the quantized information of the system measurement output and the control input, a novel networked control system model is described. This model includes many networkinduced features, such as multi-rate sampled-data, quantized signal, time-varying delay and packet dropout. By constructing suitable Lyapunov-Krasovskii functional, a less conservative stabilization criterion is established in terms of linear matrix inequalities. The quantized control strategy involves the updating values of the quantizer parameters μi(i = 1, 2)(μi take on countable sets of values which dependent on the information of the system measurement outputs and the control inputs). Furthermore, a numerical example is given to illustrate the effectiveness of the proposed method.
A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets,which cannot deal with continuous attributes.The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy.With the guarantee of consistency of decision system,the method can reduce the number of cut points and im- prove efficiency of reduction.Adopting variable precision rough information entropy as measure criterion,it has a good tolerance to noise.Experiments show that the algorithm yields satisfying reduction results.
Developing intelligent unmanned swarm systems (IUSSs) is a highly intricate process. Although current simulators and toolchains have made a notable contribution to the development of algorithms for IUSSs, they tend to concentrate on isolated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner. Furthermore, the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms. Therefore, a comprehensive solution must be developed that encompasses the entire IUSS development lifecycle. In this study, we present the RflySim ToolChain, which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs. The RflySim ToolChain employs a model-based design (MBD) approach, integrating a modeling and simulation module, a lower reliable control module, and an upper swarm decision-making module. This comprehensive integration encompasses the entire process, from modeling and simulation to testing and deployment, thereby enabling users to rapidly construct and validate IUSSs. The principal advantages of the RflySim ToolChain are as follows: it provides a comprehensive solution that meets the full-stack development needs of IUSSs; the highly modular architecture and comprehensive software development kit (SDK) facilitate the automation of the entire IUSS development process. Furthermore, the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment, which is known as the simulation to reality (Sim2Real) process. This paper presents a series of case studies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.
This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time. For this NP-hard problem, the largest sum of release date, processing time and delivery time first rule is designed to determine a certain machine for each job, and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine, and then a novel algorithm for the scheduling problem is built. To evaluate the performance of the proposed algorithm, a lower bound for the problem is proposed. The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs. The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%, therefore the solutions obtained by the proposed algorithm are very accurate.
Technology management is recognized as a key for organizations to achieve competitiveness. How to promote an organization’s technology management capability is of great significance in creating efficiencies and achieving a competitive edge. The knowledge essence of technology management capability is introduced and then the correlation between knowledge diffusion and the development of technology management capability is discussed. Further, the basic and extended dynamic models of the development of technology management capability are constructed, and is applied into an enterprise. The results show that the dynamic models can well explain how the knowledge improves the development of technology management capability, and they can be used as an useful tool by an enterprise to promote technology management capability. Finally, the managerial implications of the models are discussed.
Two protocols are presented, which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network. First, the protocol without considering the communication time-delay is presented, and by using Lyapunov stability theory, the sufficient condition of stability for this multi-agent system is presented. Further, considering the communication time-delay, the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated. The main contribution of the proposed protocols is that, as well as the velocity consensus is considered, the formation control is concerned for multi-agent systems described as the second-order equations. Finally, numerical examples are presented to illustrate the effectiveness of the proposed protocols.
The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few literature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive assumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example.
Efficiency and linearity of the microwave power amplifier are critical elements for mobile communication systems. A memory polynomial baseband predistorter based on an indirect learning architecture is presented for improving the linearity of an envelope tracing (ET) amplifier with application to a wireless transmitter. To deal with large peak-to-average ratio (PAR) problem, a clipping procedure for the input signal is employed. Then the system performance is verified by simulation results. For a single carrier wideband code division multiple access (WCDMA) signal of 16-quadrature amplitude modulation (16-QAM), about 2% improvement of the error vector magnitude (EVM) is achieved at an average output power of 45.5 dBm and gain of 10.6 dB, with adjacent channel leakage ratio (ACLR) of -64.55 dBc at offset frequency of 5 MHz. Moreover, a three-carrier WCDMA signal and a third-generation (3G) long term evolution (LTE) signal are used as test signals to demonstrate the performance of the proposed linearization scheme under different bandwidth signals.
A novel polarimetric calibration method for new target property measurement radar system is presented.Its applica- tion in the real radar system is also discussed.The analysis indicates that instantaneous polarization radar(IPR)has inherent cross-polarization measurement error.The proposed method can effectively eliminate this error,and thus enhance the polarization scattering matrix(PSM)measurement precision.The phase error caused by digital receiver’s direct IF sampling and mixing of two orthogonal polarization channels can be removed.Consequently, the inherent error of target polarization scattering measurement of the instantaneous polarization radar system is well revised.It has good reference value for further ploarimetric calibration and high practical application prospect.
Local invariant algorithm applied in downward-looking image registration, usually computes the camera’s pose relative to visual landmarks. Generally, there are three requirements in the process of image registration when using these approaches. First, the algorithm is apt to be influenced by illumination. Second, algorithm should have less computational complexity. Third, the depth information of images needs to be estimated without other sensors. This paper investigates a famous local invariant feature named speeded up robust feature (SURF), and proposes a highspeed and robust image registration and localization algorithm based on it. With supports from feature tracking and pose estimation methods, the proposed algorithm can compute camera poses under different conditions of scale, viewpoint and rotation so as to precisely localize object’s position. At last, the study makes registration experiment by scale invariant feature transform (SIFT), SURF and the proposed algorithm, and designs a method to evaluate their performances. Furthermore, this study makes object retrieval test on remote sensing video. For there is big deformation on remote sensing frames, the registration algorithm absorbs the Kanade-Lucas-Tomasi (KLT) 3-D coplanar calibration feature tracker methods, which can localize interesting targets precisely and efficiently. The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping (vSLAM) in a period of time.
A new method to extract person-independent expression feature based on higher-order singular value decomposition (HOSVD) is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression (JAFFE) database. Person-independent experimental results show the superiority of the proposed method over the existing methods.
The novel compensating method directly demodulates the signals without the carrier recovery processes, in which the carrier with original modulation frequency is used as the local coherent carrier. In this way, the phase offsets due to frequency shift are linear. Based on this premise, the compensation processes are: firstly, the phase offsets between the base band neighbor-symbols after clock recovery are unbiasedly estimated among the reference symbols; then, the receiving signals symbols are adjusted by the phase estimation value; finally, the phase offsets after adjusting are compensated by the least mean squares (LMS) algorithm. In order to express the compensation processes and ability clearly, the quadrature phase shift keying (QPSK) modulation signals are regarded as examples for Matlab simulation. BER simulations are carried out using the Monte-Carlo method. The learning curves are obtained to study the algorithm’s convergence ability. The constellation figures are also simulated to observe the compensation results directly.
To seek for lower-dimensional chaotic systems that have complex topological attractor structure with simple algebraic system structure, a new chaotic system of three-dimensional autonomous ordinary differential equations is presented. The new system has simple algebraic structure, and can display a 2-scroll attractor with complex topological structure, which is different from the Lorenz’s, Chen’s and L¨u’s attractors. By introducing a linear state feedback controller, the system can be controlled to generate a hyperchaotic attractor. The novel chaotic attractor, hyperchaotic attractor and dynamical behaviors of corresponding systems are further investigated by employing Lyapunov exponent spectrum, bifurcation diagram, Poincar′e mapping and phase portrait, etc., and then verified by simulating an experimental circuit.
Smooth support vector machine (SSVM) changs the normal support vector machine (SVM) into the unconstrained optimization by using the smooth sigmoid function. The method can be solved under the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and the Newdon-Armijio (NA) algorithm easily, however the accuracy of sigmoid function is not as good as that of polynomial smooth function. Furthermore, the method cannot reduce the influence of outliers or noise in dataset. A fuzzy smooth support vector machine (FSSVM) with fuzzy membership and polynomial smooth functions is introduced into the SVM. The fuzzy membership considers the contribution rate of each sample to the optimal separating hyperplane and makes the optimization problem more accurate at the inflection point. Those changes play a positive role on trials. The results of the experiments show that those FSSVMs can obtain a better accuracy and consume the shorter time than SSVM and lagrange support vector machine (LSVM).
In low Earth orbit (LEO) satellite networks, on-board energy resources of each satellite are extremely limited. And with the increase of the node number and the traffic transmission pressure, the energy consumption in the networks presents uneven distribution. To achieve energy balance in networks, an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor (DEF) is proposed. The DEF is defined as the function of the inter-satellite link distance and the cumulative network energy consumption ratio. According to the minimum sum of DEF on inter-satellite links, an energy consumption balancing algorithm based on DEF is proposed, which can realize dynamic traffic transmission optimization of multiple traffic services. It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network, make full use of idle nodes with low energy consumption, and optimize the energy consumption distribution of the whole network according to the continuous iterations of each traffic service flow. Simulation results show that, compared with the traditional shortest path algorithm, the proposed method can improve the balancing performance of nodes by 75% under certain traffic pressure, and realize the optimization of energy consumption balancing of the whole network.
Traditionally, beamforming using fractional Fourier transform (FrFT) involves a trial-and-error based FrFT order selection which is impractical. A new numerical order selection scheme is presented based on fractional power spectra (FrFT moment) of the linear chirp signal. This method can adaptively determine the optimum FrFT order by maximizing the second-order central FrFT moment. This makes the desired chirp signal substantially concentrated whereas the noise is rejected considerably. This improves the mean square error minimization beamformer by reducing effectively the signal-noise cross terms due to the finite data length de-correlation operation. Simulation results show that the new method works well under a wide range of signal to noise ratio and signal to interference ratio.
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.
The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated (LFM) signals. Then, a novel integrated processing scheme is proposed to resolve the delay-Doppler coupling effect in LFM pulse compression. Therefore the range and radial velocity of the target can be simultaneously estimated with a narrowband LFM pulse. Finally, numerical simulation results demonstrate the effectiveness and good performance of the proposed method.
Inverse synthetic aperture radar (ISAR) imaging of ship targets is very important in the national defense. For the high maneuverability of ship targets, the Doppler frequency shift of the received signal is time-varying, which will degrade the ISAR image quality for the traditional range-Doppler (RD) algorithm. In this paper, the received signal in a range bin is characterized as the multi-component polynomial phase signal (PPS) after the motion compensation, and a new approach of time-frequency representation, generalized polynomial Wigner-Ville distribution (GPWVD), is proposed for the azimuth focusing. The GPWVD is based on the exponential matched-phase (EMP) principle. Compared with the conventional polynomial Wigner-Ville distribution (PWVD), the EMP principle transfers the non-integer lag coefficients of the PWVD to the position of the exponential of the signal, and the interpolation can be avoided completely. For the GPWVD, the cross-terms between multi-component signals can be reduced by decomposing the GPWVD into the convolution of Wigner-Ville distribution (WVD) and the spectrum of phase adjust functions. The GPWVD is used in the ISAR imaging of ship targets, and the high quality instantaneous ISAR images can be obtained. Simulation results and measurement data demonstrate the effectiveness of the proposed new method.
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 computationally efficient soft-output detector with lattice-reduction(LR)for the multiple-input multiple-output(MIMO) systems is proposed.In the proposed scheme,the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure.With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.
Usually the polarization of the interference and the target backscattering may vary constantly, so the optimal receiving polarization of the polarization filter should be recalculated, which makes the filter realization very difficult. Also the predict method of the necessary parameters is not explained in most papers, which makes the polarization filter realization impossible. A novel modified interference suppression (MIS) polarization filter is proposed, which resolves these problems by a new polarization designed strategy. The computation of this polarization filter is easy in most conditions, and the necessary parameters estimation method in real time is introduced, which makes polarization filter design possible.
The globally exponential stability of nonlinear impulsive networked control systems (NINCS) with time delay and packet dropouts is investigated. By applying Lyapunov function theory, sufficient conditions on the global exponential stability are derived by introducing a comparison system and estimating the corresponding Cauchy matrix. An impulsive controller is explicitly designed to achieve exponential stability and ensure state converge with a given decay rate for the system. The Lorenz oscillator system is presented as a numerical example to illustrate the theoretical results and effectiveness of the proposed controller design procedure.
The electromagnetic scattering of chiral metamaterials is simulated with the Mie series method. Based on the spherical harmonics vector function in chiral metamaterials, the electromagnetic fields inside and outside of chiral metamaterials sphere are expanded. By applying the continuous boundary condition between the chiral metamaterials and surrounding medium, and the transformation from linearly to circularly polarized electric field components, the co-polarized and cross-polarized bistatic radar cross scattering (RCS) of chiral metamaterials sphere are given. How to overcome the instability of chiral metamaterials sphere of Mie series formula is discussed. The electromagnetic scattering of chiral metamaterials, normal media and metamaterials are compared. The numerical results show that the existence of chirality ξ of chiral etamaterials can decrease the bistatic RCS compared with the same size as normal media sphere.
High complexity and uncertainty of air combat pose significant challenges to target intention prediction. Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns. Accordingly, this study proposes a Mogrifier gate recurrent unit-D (Mog-GRU-D) model to address the combat target intention prediction issue under the incomplete information condition. The proposed model directly processes missing data while reducing the independence between inputs and output states. A total of 1200 samples from twelve continuous moments are captured through the combat simulation system, each of which consists of seven dimensional features. To benchmark the experiment, a missing valued dataset has been generated by randomly removing 20% of the original data. Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25% when dealing with incomplete information. This study provides possible interpretations for the principle of target interactive mechanism, highlighting the model’s effectiveness in potential air warfare implementation.
As commercial drone delivery becomes increasingly popular, the extension of the vehicle routing problem with drones (VRPD) is emerging as an optimization problem of interests. This paper studies a variant of VRPD in multi-trip and multi-drop (VRP-mmD). The problem aims at making schedules for the trucks and drones such that the total travel time is minimized. This paper formulate the problem with a mixed integer programming model and propose a two-phase algorithm, i.e., a parallel route construction heuristic (PRCH) for the first phase and an adaptive neighbor searching heuristic (ANSH) for the second phase. The PRCH generates an initial solution by concurrently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase. Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase. Numerical tests on some benchmark data are conducted to verify the performance of the algorithm. The results show that the proposed algorithm can found better solutions than some state-of-the-art methods for all instances. Moreover, an extensive analysis highlights the stability of the proposed algorithm.
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.
