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).
Remote tracking for mobile targets is one of the most
important applications in wireless sensor networks (WSNs). A
target tracking protocol– exponential distributed predictive tracking
(EDPT) is proposed. To reduce energy waste and response time,
an improved predictive algorithm–exponential smoothing predictive
algorithm (ESPA) is presented. With the aid of an additive
proportion and differential (PD) controller, ESPA decreases the
system predictive delay effectively. As a recovery mechanism, an
optimal searching radius (OSR) algorithm is applied to calculate
the optimal radius of the recovery zone. The simulation results validate
that the proposed EDPT protocol performes better in terms of
track failed ratio, energy waste ratio and enlarged sensing nodes
An adaptive actuator failure compensation control scheme is developed using an indirect adaptive control method, by calculating the controller parameters from adaptive estimates of system parameters and actuator failure parameters. A key technical issue is how to deal with the actuator failure uncertainties such as failure pattern, time and values. A complete parametrization covering all possible failures is used to solve this issue for adaptive parameter estimation. A simultaneous mapping from the estimated system/failure parameters to the controller parameters is employed to make the control system capable of ensuring the desired system performance under failures, which is verified by simulation results.
This paper investigates the feedback control of hidden Markov process (HMP) in the face of loss of some observation processes. The control action facilitates or impedes some particular transitions from an inferred current state in the attempt to maximize the probability that the HMP is driven to a desirable absorbing state. This control problem is motivated by the need for judicious resource allocation to win an air operation involving two opposing forces. The effectiveness of a receding horizon control scheme based on the inferred discrete state is examined. Tolerance to loss of sensors that help determine the state of the air operation is achieved through a decentralized scheme that estimates a continuous state from measurements of linear models with additive noise. The discrete state of the HMP is identified using three well-known detection schemes. The sub-optimal control policy based on the detected state is implemented on-line in a closed-loop, where the air operation is simulated as a stochastic process with SimEvents, and the measurement process is simulated for a range of single sensor loss rates.
This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems. The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators, each corresponding to a particular fault type. Adaptive thresholds for fault detection and isolation are presented. Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived. A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.
This paper is to explore further results for total measurable fault information-based residual (ToMFIR) approach to fault detection in dynamic systems. The ToMFIR contains the essential fault information and remains unaffected by control actions in a closed-loop system. It is composed of controller residual and output residual and some of further results are developed in frequency domain. Besides the ability of detecting actuator and sensor faults, it is able to detect faults/failures resulting from the computer used for control purpose that generates control signals. Currently, all of existing fault detection schemes cannot achieve the same task at all. A practical DC motor example, with a PID controller, is used to demonstrate the effectiveness of the ToMFIR-based fault detection. A comparison with the standard observer-based technique is also provided.
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.
An application of the multiobjective fault detection and isolation (FDI) approach to an air-breathing hypersonic vehicle (HSV) longitudinal dynamics subject to disturbances is presented. Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects, variable operating conditions and possible failures of system components. A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors. To detect and isolate multiple actuator/sensor failures, a faulty linear parameter-varying (LPV) model of HSV is derived by converting actuator/system component faults into equivalent sensor faults. Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals. The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.
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.
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.
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.
A novel satellite fault diagnosis scheme is presented based on the predictive filter and empirical mode composition (EMD). First, the predictive filter is utilized to obtain the fault estimation, which is corrupted by noise. Then the EMD method is introduced to decompose the fault estimation into a finite number of intrinsic mode functions and extract the trend of faults for fault diagnosis. The proposed scheme has the ability of diagnosing both abrupt and incipient faults of the actuator in a satellite attitude control subsystem. A mathematical simulation is given to illustrate the effectiveness of the proposed scheme.
A review on fault-tolerant control (FTC) for near space vehicle (NSV) is presented. First, the concept of near space is introduced, the background of NSV is emphasized, and the model characteristics of NSV in faulty case are investigated. Then, a comparison of different existing approaches is briefly carried out, and achievements on the current research in this field are also presented in the view of the practical application. Furthermore, several existing advanced FTC results for nonlinear flight control systems are given. Finally, the recent literature of NSV are presented to provide an overall view of future developments in this area.
Conventional PI control encounters some problems when dealing with large lag process in the presence of parameter uncertainties. For the typical first-order process, an observerbased linear active disturbance rejection control (LADRC) scheme is presented to cope with the difficulties, and a reduced-order observer scheme is proposed further. Some quantitative dynamic results with regard to non-overshoot characteristics are obtained. Finally, the performance boundaries of LADRC and PI control are explicitly compared with each other, which shows that the former is more superior in most cases.
Based on multiple unmanned aerial vehicles (UAVs) flight at a constant altitude, a fault-tolerant cooperative localization algorithm against global positioning system (GPS) signal loss due to GPS receiver malfunction is proposed. Contrast to the traditional means with single UAV, the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications. Firstly, for re-localizing an UAV with a malfunction in its GPS receiver, an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed. Secondly, by using the relative ranges from the faulty UAV to the other three UAVs, its horizontal location can be determined after the GPS signal is lost. In order to improve an accuracy of the localization, a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss. The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning (HDOP) automatically. Then, during each discrete computing time step, the best reference points are selected adaptively by minimizing the HDOP. Finally, two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.
Since any disturbance and fault may lead to significant performance degradation in practical dynamical systems, it is essential for a system to be robust to disturbances but sensitive to faults. For this purpose, this paper proposes a robust fault-detection filter for linear discrete time-varying systems. The algorithm uses H∞ estimator to minimize the worst possible amplification from disturbances to estimate errors, and H− index to maximize the minimum effect of faults on the residual output of the filter. This approach is applied to the MEMS-based INS/GPS. And simulation results show that the new algorithm can reduce the effect of unknown disturbances and has a high sensitivity to faults.
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.
This paper addresses the problem of fault detection (FD) for networked systems with access constraints and packet dropouts. Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process, respectively. Performance indexes H∞ and H− are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults, respectively. By using a mode-dependent fault detection filter (FDF) as residual generator, the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints. A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired FDF can also be characterized. A numerical example is exploited to show the usefulness of the proposed results.
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.
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.
In detecting system fault algorithms, the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters. The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system. The simulation result shows that the algorithm can accurately detect the fault information of global positioning system (GPS), eliminate the influence of false alarm and missed detection on filter, and enhance fault tolerance of integrated navigation systems.