Multiple maneuvering targets signal processing in high frequency radar is challenging due to the following difficulties: the interference between signals is severe because of significant spread of the target Doppler spectrum, the low signal to clutter ratio (SCR) environment degrades the performance of signal processing algorithms. This paper addresses this challenging problem by using an S2-method and an adaptive clutter rejection scheme. The proposed S2-method improves the S-method by eliminating interference between signals, and thus it enables multi-target signals to be reconstructed individually. The proposed adaptive clutter rejection scheme is based on an adaptive notch filter, which is designed according to the envelop of the clutter spectrum. Experiments with simulated targets added into radar sea clutter echo and real air target data illustrate the effectiveness of the proposed method.
To solve the resource-constrained project scheduling problem (RCPSP), a hybrid ant colony optimization (HACO) approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved. The HACO is tested on the set of large benchmark problems from the project scheduling problem library (PSPLIB). The computational result shows that the proposed algorithm can improve the quality of the schedules efficiently.
Electromagnetic scattering from targets situated in half space is solved by applying fast inhomogeneous plane wave algorithm combined with a tabulation and interpolation method. The integral equation is set up based on derivation of dyadic Green’s functions in this environment. The coupling is divided into nearby region and well-separated region by grouping. The Green’s function can be divided into two parts: primary term and reflected term. In the well-separated region, the two terms are both expressed as Sommerfeld integral, which can be accelerated by deforming integral path and taking interpolation and extrapolation. For the nearby region, the direct Sommerfeld integral makes the filling of impedance matrix time-expensive. A tabulation and interpolation method is applied to speed up this process. This infinite integral is pre-computed in sampling region, and a two-dimensional table is then set up. The impedance elements can then be obtained by interpolation. Numerical results demonstrate the accuracy and efficiency of this algorithm.
A new robust fault-tolerant controller scheme integrating a main controller and a compensator for the self-repairing flight control system is discussed. The main controller is designed for high performance of the original faultless system. The compensating controller can be seen as a standalone loop added to the system to compensate the effects of fault guaranteeing the stability of the system. A design method is proposed using nonlinear dynamic inverse control as the main controller and nonlinear extended state observer-based compensator. The stability of the whole closed-loop system is analyzed. Feasibility and validity of the new controller is demonstrated with an aircraft simulation example.
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.
To minimize the total transmit power for multicast service in an orthogonal frequency division multiplexing (OFDM) downlink system, resource allocation algorithms that adaptively allocate subcarriers and bits are proposed. The proposed algorithms select users with good channel conditions for each subcarrier to reduce the transmit power, while guaranteeing each user’s instantaneous minimum rate requirement. The resource allocation problem is first formulated as an integer programming (IP) problem, and then, a full search algorithm that achieves an optimal solution is presented. To reduce the computation load, a suboptimal algorithm is proposed. This suboptimal algorithm decouples the joint resource allocation problem by separating subcarrier and bit allocation. Greedy-like algorithms are employed in both procedures. Simulation results illustrate that the proposed algorithms can significantly reduce the transmit power compared with the conventional multicast approach and the performance of the suboptimal algorithm is close to the optimum.
A novel decentralized indirect adaptive output feedback fuzzy controller is developed for a class of large-scale uncertain nonlinear systems using error filtering. By the properly filtering of the observation error dynamics, the strictly positive-real condition is guaranteed to hold such that the proposed output feedback and adaptation mechanisms are practicable in practice owing to the fact that its implementation does not require the observation error vector itself any more, which corrects the impracticable schemes in the previous literature involved. The presented control algorithm can ensure that all the signals of the closed-loop large-scale system keep uniformly ultimately bounded and that the tracking error converges to zero asymptotically. The decentralized output feedback fuzzy controller can be applied to address the longitudinal control problem of a string of vehicles within an automated highway system (AHS) and the effectiveness of the design procedure is supported by simulation results.
A condition-based maintenance model for gamma deteriorating system under continuous inspection is studied. This methodology uses a gamma distribution to model the material degradation, and the impact of imperfect maintenance actions on the system reliability is investigated. The state of a degrading system immediately after the imperfect maintenance action is assumed as a random variable and the maintenance time follows a geometric process. Furthermore, the explicit expressions for the long-run average cost and availability per unit time of the system are evaluated, an optimal policy (ξ∗) could be determined numerically or analytically according to the optimization model. At last, a numerical example for a degrading system modeled by a gamma process is presented to demonstrate the use of this policy in practical applications.
Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators are also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.
In this paper, a bandwidth-adjustable extended state observer (ABESO) is proposed for the systems with measurement noise. It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise, which conflicts with observation accuracy. Therefore, we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system. The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error. When the tracking error decreases, the bandwidth decreases to suppress the noise, otherwise the bandwidth does not change. It is proven that the error dynamics are bounded and converge in finite time. The relationship between the upper bound of the estimation error and the scaling factor is given. When the scaling factor is less than 1, the ABESO has higher estimation accuracy than the linear extended state observer (LESO). Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments. The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.
For time-of-flight (TOF) light detection and ranging (LiDAR), a three-channel high-performance transimpedance amplifier (TIA) with high immunity to input load capacitance is presented. A regulated cascade (RGC) as the input stage is at the core of the complementary metal oxide semiconductor (CMOS) circuit chip, giving it more immunity to input photodiode detectors. A simple smart output interface acting as a feedback structure, which is rarely found in other designs, reduces the chip size and power consumption simultaneously. The circuit is designed using a 0.5 μm CMOS process technology to achieve low cost. The device delivers a 33.87 dB? transimpedance gain at 350 MHz. With a higher input load capacitance, it shows a ?3 dB bandwidth of 461 MHz, indicating a better detector tolerance at the front end of the system. Under a 3.3 V supply voltage, the device consumes 5.2 mW, and the total chip area with three channels is 402.8×597.0 μm2 (including the test pads).
A new filtering method for SAR data de-noising using wavelet support vector regression (WSVR) is developed. On the basis of the grey scale distribution character of SAR imagery, the logarithmic SAR image as a noise polluted signal is taken and the noise model assumption in logarithmic domain with Gaussian noise and impact noise is proposed. Based on the better performance of support vector regression (SVR) for complex signal approximation and the wavelet for signal detail expression, the wavelet kernel function is chosen as support vector kernel function. Then the logarithmic SAR image is regressed with WSVR. Furthermore the regression distance is used as a judgment index of the noise type. According to the judgment of noise type every pixel can be adaptively de-noised with different filters. Through an approximation experiment for a one-dimensional complex signal, the feasibility of SAR data regression based on WSVR is confirmed. Afterward the SAR image is treated as a two-dimensional continuous signal and filtered by an SVR with wavelet kernel function. The results show that the method proposed here reduces the radar speckle noise effectively while maintaining edge features and details well.
The relationship between the technique by statedependent Riccati equations (SDRE) and Hamilton-Jacobi-Isaacs (HJI) equations for nonlinear H∞ control design is investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equivalent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.
A 3D motion and geometric information system of single-antenna radar is proposed, which can be supported by spotlight synthetic aperture radar (SAR) system and inverse SAR (ISAR) system involving relative 3D motion of the rigid target. In this system, applying the geometry invariance of the rigid target, the unknown 3D shape and motion of the radar target can be reconstructed from the 1D range data of some scatterers extracted from the high-resolution range image. Compared with the current 1D-to-3D algorithm, in the proposed algorithm, the requirement of the 1D range data is expanded to incomplete formation involving large angular motion of the target and hence, the quantity of the scatterers and the abundance of 3D motion are enriched. Furthermore, with the three selected affine coordinates fixed, the multi-solution problem of the reconstruction is solved and the technique of nonlinear optimization can be successfully utilized in the system. Two simulations are implemented which verify the higher robustness of the system and the better performance of the 3D reconstruction for the radar target with unknown relative motion.
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.
The command and control (C2) is a decision-making process based on human cognition, which contains operational, physical, and human characteristics, so it takes on uncertainty and complexity. As a decision support approach, Bayesian networks (BNs) provide a framework in which a decision is made by combining the experts’ knowledge and the specific data. In addition, an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker. The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets (CPNs), and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.
H-infinity estimator is generally implemented in timevariant state-space models, but it leads to high complexity when the model is used for multiple input multiple output with orthogonal frequency division multiplexing (MIMO-OFDM) systems. Thus, an H-infinity estimator over time-invariant system models is proposed, which modifies the Krein space accordingly. In order to avoid the large matrix inversion and multiplication required in each OFDM symbol from different transmit antennas, expectation maximization (EM) is developed to reduce the high computational load. Joint estimation over multiple OFDM symbols is used to resist the high pilot overhead generated by the increasing number of transmit antennas. Finally, the performance of the proposed estimator is enhanced via an angle-domain process. Through performance analysis and simulation experiments, it is indicated that the proposed algorithm has a better mean square error (MSE) and bit error rate (BER) performance than the optimal least square (LS) estimator. Joint estimation over multiple OFDM symbols can not only reduce the pilot overhead but also promote the channel performance. What is more, an obvious improvement can be obtained by using the angle-domain filter.
The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.
This paper presents a scheme of fault diagnosis for flexible satellites during orbit maneuver. The main contribution of the paper is related to the design of the nonlinear input observer which can avoid false alarm arising from the disturbance from orbit control force. The effects of orbit control force on the fault diagnosis system for satellite attitude control systems, including the disturbing torque caused by the misalignments and the model uncertainty caused by the fuel consumed, are discussed, where standard Luenberger observer cannot work well. Then the nonlinear unknown input observer is proposed to decouple faults from disturbance. Besides, a linear matrix inequality approach is adopted to reduce the effect of nonlinear part and model uncertainties on the observer. The numerical and semi-physical simulation demonstrates the effectiveness of the proposed observer for the fault diagnosis system of the satellite during orbit maneuver.
The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM(1,2) with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.
With the development of the monitoring technology, it is more and more common that the system is continuously monitored. Therefore, the research on the maintenance optimization of the continuously monitored deterioration system is important. The deterioration process of the discussed system is described by a Gamma process. The predictive maintenance is considered to be imperfect and formulated. The expected interval of two continuous preventive maintenances is derived. Then, the maintenance optimization model of the continuously monitored deterioration system is presented. In the model, the minimization of the expected operational cost per unit time and the maximization of the system availability are the optimization objectives. The improved ideal point method with the normalized objective functions is employed to solve the proposed model. The validity and sensitivity of the proposed multiobjective maintenance optimization model are analyzed by a numerical example.
Aiming at the characteristics of multi-stage and (extremely) small samples of the identification problem of key effectiveness indexes of weapon equipment system-of-systems (WESoS), a Bayesian intelligent identification and inference model for system effectiveness assessment indexes based on dynamic grey incidence is proposed. The method uses multilayer Bayesian techniques, makes full use of historical statistics and empirical information, and determines the Bayesian estimation of the incidence degree of indexes, which effectively solves the difficulties of small sample size of effectiveness indexes and difficulty in obtaining incidence rules between indexes. Secondly, The method quantifies the incidence relationship between evaluation indexes and combat effectiveness based on Bayesian posterior grey incidence, and then identifies key system effectiveness evaluation indexes. Finally, the proposed method is applied to a case of screening key effectiveness indexes of a missile defensive system, and the analysis results show that the proposed method can fuse multi-moment information and extract multi-stage key indexes, and has good data extraction capability in the case of small samples.
Discrete event system (DES) models promote system engineering, including system design, verification, and assessment. The advancement in manufacturing technology has endowed us to fabricate complex industrial systems. Consequently, the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative. Moreover, industrial systems are no longer quiescent, thus the intelligent operations of the systems should be dynamically specified in the model. In this paper, the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model, and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model. In traditional modeling approaches, the change or addition of specifications always necessitates the complete resubmission of the system model, a resource-consuming and error-prone process. Compared with traditional approaches, our approach has three remarkable advantages: (i) an established Boolean semantic can be fitful for all kinds of systems; (ii) there is no need to resubmit the system model whenever there is a change or addition of the operations; (iii) multiple specifying tasks can be easily achieved by continuously adding a new semantic. Thus, this general modeling approach has wide potential for future complex and intelligent industrial systems.
A robust reliability method for stability analysis and reliability-based stabilization of time-delay dynamic systems with uncertain but bounded parameters is presented by treating the uncertain parameters as interval variables. The performance function used for robust reliability analysis is defined by a delayindependent stability criterion. The design of robust controllers is carried out by solving a reliability-based optimization problem in which the control cost satisfying design requirements is minimized. This kind of treatment makes it possible to achieve a balance between the reliability and control cost in the design of controller when uncertainties must be taken into account. By the method, a robust reliability measure of the degree of stability of a time-delay uncertain system can be provided, and the maximum robustness bounds of uncertain parameters such that the time-delay system to be stable can be obtained. All the procedures are based on the linear matrix inequality approach and therefore can be carried out conveniently. The effectiveness and feasibility of the proposed method are demonstrated with two practical examples. It is shown by numerical simulations and comparison that it is meaningful to take the robust reliability into account in the control design of uncertain systems.
This paper derives the extended ambiguity function for a bistatic multiple-input multiple-output (MIMO) radar system, which includes the whole radar system parameters: geometric sensor configuration, waveforms, range, range rate, target scattering and noise characteristics. Recent research indicates the potential parameter estimate performance of bistatic MIMO radars. And this ambiguity function can be used to analyze the parameter estimate performance for the relationship with the Cramer-Rao bounds of the estimated parameters. Finally, some examples are given to demonstrate the good parameter estimate performance of the bistatic MIMO radar, using the quasi-orthogonal waveforms based on Lorenz chaotic systems.
An improved differential evolution (IDE) algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem (RCPSP) with the objective of minimizing project duration. Activities priorities for scheduling are represented by individual vectors and a serial scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated. To investigate the performance of the IDE-based approach for the RCPSP, it is compared against the meta-heuristic methods of hybrid genetic algorithm (HGA), particle swarm optimization (PSO) and several well selected heuristics. The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.
For reducing the inter-user interference in multi-user multiple-input multiple-output (MU-MIMO) wireless communication systems, e.g., MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is often desirable to the complex preprocessing at the transmitter. This paper proposes a multi-user beamforming algorithm with sub-codebook selection. Based on the minimal leakage criterion, the codebook selection, limited feed-forward and minimum mean square error (MMSE) detection are combined in the proposed algorithm. This avoids the complex channel matrix decomposition and inversion. Consequently, the computational complexity at the transmitter is significantly reduced. Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.
A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker (KKT) conditions. To cope with the complementarity constraints, a binary encoding scheme is adopted for KKT multipliers, and then the complementarity slackness problem is simplified to successive quadratic programming problems, which can be solved by many algorithms available. Based on 0−1 binary encoding, an orthogonal genetic algorithm, in which the orthogonal experimental design with both two-level orthogonal array and factor analysis is used as crossover operator, is proposed. Numerical experiments on 10 benchmark examples show that the orthogonal genetic algorithm can find global optimal solutions of quadratic bilevel programming problems with high accuracy in a small number of iterations.
For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic nonlinear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.
Quorum systems have been used to solve the problem of data consistency in distributed fault-tolerance systems. But when intrusions occur, traditional quorum systems have some disadvantages. For example, synchronous quorum systems are subject to DOS attacks, while asynchronous quorum systems need a larger system size (at least 3f+1 for generic data, and f fewer for self-verifying data). In order to solve the problems above, an intrusion-tolerance quorum system (ITQS) of hybrid time model based on trust timely computing base is presented (TTCB). The TTCB is a trust secure real-time component inside the server with a well defined interface and separated from the operation system. It is in the synchronous communication environment while the application layer in the server deals with read-write requests and executes update-copy protocols asynchronously. The architectural hybridization of synchrony and asynchrony can achieve the data consistency and availability correctly. We also build two kinds of ITQSes based on TTCB, i.e., the symmetrical and the asymmetrical TTCB quorum systems. In the performance evaluations, we show that TTCB quorum systems are of smaller size, lower load and higher availability.
Fuzzy c-means(FCM)algorithm is one of the most popular methods for image segmentation.However,the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image.An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm.The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering.The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion.It is not only effective to remove the noise spots but also can reduce the misclassified pixels.Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance. It is called the induced heavy ordered weighted averaging (OWA) distance (IHOWAD) operator. This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA (IHOWA) operator, the induced OWA distance (IOWAD) operator and the heavy OWA distance (HOWAD) operator. This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD (IGHOWAD) operator and the Quasi-IHOWAD operator. An application of the new approach in a decision making problem regarding the selection of strategies is developed.
An improved particle swarm optimization (PSO) algorithm is proposed to train the fuzzy support vector machine (FSVM) for pattern multi-classification. In the improved algorithm, the particles studies not only from itself and the best one but also from the mean value of some other particles. In addition, adaptive mutation was introduced to reduce the rate of premature convergence. The experimental results on the synthetic aperture radar (SAR) target recognition of moving and stationary target acquisition and recognition (MSTAR) dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
Coordinate descent method is a unconstrained optimization technique. When it is applied to support vector machine (SVM), at each step the method updates one component of w by solving a one-variable sub-problem while fixing other components. All components of w update after one iteration. Then go to next iteration. Though the method converges and converges fast in the beginning, it converges slow for final convergence. To improve the speed of final convergence of coordinate descent method, Hooke and Jeeves algorithm which adds pattern search after every iteration in coordinate descent method was applied to SVM and a global Newton algorithm was used to solve one-variable sub-problems. We proved the convergence of the algorithm. Experimental results show Hooke and Jeeves’ method does accelerate convergence specially for final convergence and achieves higher testing accuracy more quickly in classification.
During high-speed flight, both thermal and mechanical loads can degrade the electrical performance of the antenna-radome system, which can subsequently affect the performance of the guidance system. This paper presents a method for evaluating the electrical performance of the radome when subjected to thermo-mechanical-electrical (TME) coupling. The method involves establishing a TME coupling model (TME-CM) based on the TME sharing mesh model (TME-SMM) generated by the tetrahedral mesh partitioning of the radome structure. The effects of dielectric temperature drift and structural deformation on the radome’s electrical performance are also considered. Firstly, the temperature field of the radome is obtained by transient thermal analysis while the deformation field of the radome is obtained by static analysis. Subsequently, the dielectric variation and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM. The three-dimensional (3D) ray tracing method with the aperture integration technique is used to calculate the radome’s electrical performance. A representative example is provided to illustrate the superiority and necessity of the proposed method. This is achieved by calculating and analyzing the changes in the radome’s electrical performance over time during high-speed flight.
Nowadays manufacturers are facing fierce challenge. Apart from the products, providing customers with multiple maintenance options in the service contract becomes more popular, since it can help to improve customer satisfaction, and ultimately promote sales and maximize profit for the manufacturer. By considering the combinations of corrective maintenance and preventive maintenance, totally three types of maintenance service contracts are designed. Moreover, attractive incentive and penalty mechanisms are adopted in the contracts. On this basis, Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers, and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation. Numerical experiments are conducted. The results show that by taking into account the incentive and penalty mechanisms, the revenue can be improved for both the customers and manufacturer. Moreover, with the increase of repair rate and improvement factor in the preventive maintenance, the revenue will increase gradually for both the parties.
A linear matrix inequality(LMI)-based sliding surface design method for integral sliding mode control of uncertain time- delay systems with mismatching uncertainties is proposed.The uncertain time-delay system under consideration may have mis- matching norm bounded uncertainties in the state matrix as well as the input matrix.A sufficient condition for the existence of a sliding surface is given to guarantee asymptotic stability of the full order sliding mode dynamics.An LMI characterization of the slid- ing surface is given,together with an integral sliding mode control law guaranteeing the existence of a sliding mode from the initial time.Finally,a simulation is given to show the effectiveness of the proposed method.
This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable (EIV) model. The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data. Under the structural density assumption, the C-step technique borrowed from the Rousseeuw’s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization. To eliminate the model ambiguities of the multiple-structural data, statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation. Experiments show that the efficiency and robustness of the proposed algorithm. This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable (EIV) model. The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data. Under the structural density assumption, the C-step technique borrowed from the Rousseeuw’s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization. To eliminate the model ambiguities of the multiple-structural data, statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation. Experiments show that the efficiency and robustness of the proposed algorithm.
