This article studies the issue of resilient event-triggered (RET)-based security controller design for nonlinear networked control systems (NCSs) described by interval type-2 (IT2) fuzzy models ...subject to nonperiodic denial of service (DoS) attacks. Under the nonperiodic DoS attacks, the state error caused by the packets loss phenomenon is transformed into an uncertain variable in the designed event-triggered condition. Then, an RET strategy based on the uncertain event-triggered variable is firstly proposed for the nonlinear NCSs. The existing results that utilized the hybrid triggered scheme have the defect of complex control structure, and most of the security compensation methods for handling the impacts caused by DoS attacks need to transmit some compensation data when the DoS attacks disappear, which may lead to large performance loss of the systems. Different from these existing results, the proposed RET strategy can transmit the necessary packets to the controller under nonperiodic DoS attacks to reduce the performance loss of the systems and a new security controller subject to the RET scheme and mismatched membership functions is designed to simplify the network control structure under DoS attacks. Finally, some simulation results are utilized to testify the advantages of the presented approach.
In this paper, we investigate the stability and performance of the interval type-2 (IT2) polynomial fuzzy-model-based tracking control system, formed by an IT2 polynomial fuzzy model and an IT2 ...polynomial fuzzy controller, based on the output feedback and sampled-data structure. IT2 fuzzy sets are employed to capture the uncertainties of the nonlinear plant. Furthermore, considering the digital implementation of control strategy and only the system outputs are available, the IT2 polynomial fuzzy controller is of discrete time and output-feedback type. Both membership-function-independent and membership-function-dependent stability analysis, with the consideration of H ∞ performance index, are conducted to develop stability conditions in terms of sum-of-square based on Lyapunov stability theory. The information of membership functions, system states, and sampling process are included in the stability analysis for the relaxation of stability conditions. Simulation examples are presented to verify the effectiveness of the proposed tracking control approach.
Till now, there are lots of stability and stabilization results about Takagi-Sugeno (T-S) fuzzy systems with time delay, but most of them are independent of the analysis of membership functions. ...Since the membership functions are an essential component to make a fuzzy system different from others, the conditions without its information are conservative. In this brief paper, a new Lyapunov-Krasovskii functional is designed to investigate the stability and stabilization of continuous-time T-S fuzzy systems with time delay. Different from the existing results in the literature, the integrand of the Lyapunov-Krasovskii functional in this paper depends not only on the integral variable, but also on the membership functions, and thus, the information of the time derivative of membership functions can also be used to reduce the conservativeness of finding the maximum delay bounds. Utilizing the information of the time derivative of membership, a number of controllers are designed according to their sign, and then, a switching idea is applied to stabilize the fuzzy system. In the end, two examples are given to illustrate the feasibility and validity of the design and analysis.
In this technical note, the consensus control problem is investigated for a class of discrete time-varying stochastic multi-agent system subject to sensor saturations. An event-based mechanism is ...adopted where each agent updates the control input signal only when the pre-specified triggering condition is violated. To reflect the time-varying manner and characterize the transient consensus behavior, a new index for mean-square consensus is put forward to quantify the deviation level from individual agent to the average value of all agents' states. For a fixed network topology, the aim of the proposed problem is to design time-varying output-feedback controllers such that, at each time step, the mean-square consensus index of the closed-loop multi-agent system satisfies the pre-specified upper bound constraints subject to certain triggering mechanism. Both the existence conditions and the explicit expression of the desired controllers are established by resorting to the solutions to a set of recursive matrix inequalities. An illustrative simulation example is utilized to demonstrate the usefulness of the proposed algorithms.
In this article, the singularity-free adaptive fuzzy fixed-time control problem is studied for an uncertain n -link robotic system with the position tracking error constraint. The controlled robotic ...system can be described as a multiple-input-multiple-output system.To implement the user-defined performance, an improved error conversion mechanism based on performance functions is presented such that the converted error is limited to an interval greater than zero, and an appropriate barrier Lyapunov function (BLF) is constructed to avoid the breach of position tracking error constraint. The fuzzy approximator is utilized to estimate the unknown functions. The significance and challenges of this article are to establish a new error conversion mechanism and design corresponding BLF that can be integrated into fixed-time control design to present a singularity-free adaptive fuzzy fixed-time control scheme. Benefits of the proposed adaptive fixed-time controller in comparison to the current approaches are that it cannot cause the singularity issue appearing in backstepping-based fixed-time control design and ensures quick transient response. Combining with Lyapunov stability theory, the boundedness of the closed-loop signals is ensured, and the position tracking error can be constrained in the user-defined performance boundaries. Finally, simulation results demonstrate the feasibility of the proposed control strategy.
Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 ...cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.
•COVID-19 detection using Chest X-Ray and computerised tomography (CT) images.•A type of dynamic CNN modification methods including modified MobileNet and ResNet.•99.6% test accuracy on a Chest X-Ray dataset, 99.3% test accuracy on a benchmark CT image dataset.
In this technical note, a new fault detection design scheme is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems with sensor fault based on a novel fuzzy observer. The parameter ...uncertainties can be captured by the membership functions of the IT2 fuzzy model. The premise variables of the plant are perfectly shared by the fuzzy observer. A stochastic process between the plant and the observer is considered in the system. A fault sensitive performance is established, and then sufficient conditions are obtained for determining the fuzzy observer gains. Finally, simulation results are provided to verify the effectiveness of the presented scheme.
The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear ...networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi-Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method.
In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems (MASs), stochastic disturbances and actuator faults are regarded as essential factors that hamper effective controller ...formulation and tracking precision improvement. To address these difficulties, we design an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme to achieve finite-time robust precision BCT tasks for nonlinear MASs. The parameter selection range of the improved FTPF is relaxed, which renders systems to achieve better transient performance. Benefitting from the stochastic Lyapunov stability theory, it is shown that all signals of systems are semiglobal uniformly ultimately bounded in probability, and bipartite consensus errors can satisfy the arbitrary precision with probability in the predefined time. Finally, to verify its effectiveness, the proposed control scheme is applied to BCT tasks of a group of vehicles, which manifests anticipated control performance under various uncertainties.
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.
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