This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs ...model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with <inline-formula><tex-math notation="LaTeX">H_{\infty }</tex-math></inline-formula> performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.
In this technical paper, a new Lyapunov-Krasovskii functional (LKF) is designed to study the stability of continuous-time Takagi-Sugeno fuzzy systems with time-varying delay. The integrand of the LKF ...depends on integral variable and time <inline-formula> <tex-math notation="LaTeX">{t} </tex-math></inline-formula> which can help to reduce the number of linear matrix inequalities (LMIs). Then, a new stability criterion is derived by analyzing the sign of the time derivatives of membership functions. Compared with the existing results, larger delay bounds can be obtained by applying the new criterion. In the end, two examples show the effectiveness of the conclusions.
This article studies the problem of dissipative control for singular Takagi-Sugeno fuzzy systems with time delay. In order to reduce the conservatism brought by the time delay, we choose a proper ...augmented Lyapunov-Krasovskii functional and the auxiliary-function-based integral inequality to analyze the admissibility and dissipativity of the system. The singularity of the derivative matrix of the system leads to the matrix coupling terms, which are difficult to handle in the controller design by using the common controller design methods, so we propose an iterative algorithm to solve the controller parameters. The sufficient conditions of ensuring that the closed-loop system is admissible and dissipative are presented. Finally, three persuasive examples are given to demonstrate the effectiveness of the proposed method.
This article investigates the problem of event-based decentralized adaptive fuzzy output-feedback finite-time control for the large-scale nonlinear systems. The full-state tracking error constraints, ...unmeasured states, and external disturbances are simultaneously considered in the controlled systems. The unknown auxiliary functions are modeled by using fuzzy logic systems, and a state observer is established to estimate unmeasured states. By taking a new error transformation method based on prescribed performance functions and constructing corresponding barrier Lyapunov functions, the predefined system error dynamic performance is ensured. Then, on the basis of the event-triggered control technique and the backstepping recursive design technique, a new event-based adaptive fuzzy nonsingular finite-time control strategy is proposed, and the "singularity" problem existing in backstepping design procedure is avoided. Moreover, by using the finite-time stability criterion, it is proven that the proposed control strategy can ensure the boundedness of the whole system variables and achieve all the state tracking errors evolve within the predesigned performance regions in finite time. Finally, the effectiveness of the proposed control strategy is verified by using some simulation results.
The early detection of infection is significant for the fight against the ongoing COVID-19 pandemic. Chest X-ray (CXR) imaging is an efficient screening technique via which lung infections can be ...detected. This paper aims to distinguish COVID-19 positive cases from the other four classes, including normal, tuberculosis (TB), bacterial pneumonia (BP), and viral pneumonia (VP), using CXR images. The existing COVID-19 classification researches have achieved some successes with deep learning techniques while sometimes lacking interpretability and generalization ability. Hence, we propose a two-stage classification method MANet to address these issues in computer-aided COVID-19 diagnosis. Particularly, a segmentation model predicts the masks for all CXR images to extract their lung regions at the first stage. A followed classification CNN at the second stage then classifies the segmented CXR images into five classes based only on the preserved lung regions. In this segment-based classification task, we propose the mask attention mechanism (MA) which uses the predicted masks at the first stage as spatial attention maps to adjust the features of the CNN at the second stage. The MA spatial attention maps for features calculate the percentage of masked pixels in their receptive fields, suppressing the feature values based on the overlapping rates between their receptive fields and the segmented lung regions. In evaluation, we segment out the lung regions of all CXR images through a UNet with ResNet backbone, and then perform classification on the segmented CXR images using four classic CNNs with or without MA, including ResNet34, ResNet50, VGG16, and Inceptionv3. The experimental results illustrate that the classification models with MA have higher classification accuracy, more stable training process, and better interpretability and generalization ability than those without MA. Among the evaluated classification models, ResNet50 with MA achieves the highest average test accuracy of 96.32% in three runs, and the highest one is 97.06%. Meanwhile, the attention heat maps visualized by Grad-CAM indicate that models with MA make more reliable predictions based on the pathological patterns in lung regions. This further presents the potential of MANet to provide clinicians with diagnosis assistance.
This paper deals with the problem of reliable and robust H ∞ static output feedback (SOF) controller synthesis for continuous-time nonlinear stochastic systems with actuator faults. The nonlinear ...stochastic plant is expressed by an Itô-type Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and a Markov process is employed to model the occurrence of actuator fault. The purpose is to design an admissible piecewise SOF controller, such that the resulting closed-loop system is stochastically stable with a prescribed disturbance attenuation level in an sense. Specifically, based on a Markovian Lyapunov function combined with Itô differential formula, S-procedure, and some matrix inequality convexification procedures, two new approaches to the reliable SOF controller analysis and synthesis are proposed for the underlying stochastic fuzzy-affine systems. It is shown that the existence of desired reliable controllers is fully characterized in terms of strict linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the developed methods.
This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate ...a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.
This paper proposes a novel system-augmentation approach to the delay-dependent reliable piecewise-affine ℋ ∞ static output feedback control for nonlinear systems with time-varying delay and sensor ...faults in the piecewise-Markovian-Lyapunovfunctional-based framework. The nonlinear plant is described by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and the sensor faults are characterized by a Markov process. Specifically, by applying a state-input augmentation technique, the original closed-loop system is first reformulated into a descriptor fuzzy-affine system. Based on a new piecewise-Markovian Lyapunov-Krasovskii functional, combined with a Wirtinger-based integral inequality, improved reciprocally convex inequality, and S-procedure, a novel bounded real lemma is then derived for the underlying closed-loop system. Furthermore, by taking advantage of the redundancy of descriptor system formulation, together with a linearization procedure, the piecewiseaffine controller synthesis is carried out. It is shown that the desired piecewise-affine controller gains can be attained by solving a linear matrix inequality based optimization problem. Finally, simulation examples are performed to confirm the effectiveness and less conservatism of the presented approach.
This paper is concerned with the optimal guaranteed cost sliding-mode control problem for interval type-2 (IT2) Takagi-Sugeno fuzzy systems with time-varying delays and exogenous disturbances. In the ...presence of the uncertain parameters hidden in membership functions, an adaptive method is presented to handle the time-varying weight coefficients reflecting the change of the uncertain parameters. A new integral sliding surface is presented based on the system output. By designing a novel adaptive sliding-mode controller, system perturbation or modeling error can be compensated, and the reachability of the sliding surface can be guaranteed with the ultimate uniform boundedness of the closed-loop system. Optimal conditions of an H 2 guaranteed cost function and an H ∞ performance index are established for the resulting time-delay control system. Finally, an inverted pendulum system represented by the IT2 fuzzy model is applied to illustrate the advantages and effectiveness of the proposed control scheme.
This brief paper investigates the local stabilization for continues-time Takagi-Sugeno fuzzy systems with constant time delay. In order to deal with the time delay, we design a Lyapunov-Krasovskii ...functional that is dependent on the membership function. Based on the Lyapunov-Krasovskii functional and the analysis of the time derivative of the membership function, less conservative results can be obtained; however, the Lyapunov-Krasovskii functional is designed so complicated that the Lyapunov level set is hard to be measured directly. Alternatively, two sets are obtained to estimate the local stabilization. One set is for the time-varying initial conditions and the other is for the time-invariant initial conditions. The relationship between the two sets are also discussed. In the end, two examples are given to illustrate the effectiveness of the proposed approach.
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