The DC-DC switching power converters are highly nonlinear systems. Consequently, the conventional linear controls based on averaging and linearization techniques will result in poor dynamic performance or system instability. In order to resolve this problem, in this paper a robust state feedback H∞ control is proposed for these systems under Markovian switching with mixed discrete, neutral and distributed delays. Based on the Lyapunov-Krasovskii functional theory, some required sufficient conditions are established in terms of delay-dependent linear matrix inequalities for the stochastic stability and stabilization of the considered system using some free matrices. The desired control is derived based on a convex optimization method.
This paper is concerned with the problem of delay-dependent H ∞ control for 2-D (two-dimensional) switched discrete state delay systems described by the second FM (Fornasini and Marchesini) state-space model. Firstly, some sufficient conditions for the exponential stability and weighted H ∞ disturbance attenuation performance of the underlying system are derived via the average dwell time approach. Then, based on the obtained results, a state feedback controller design is proposed to guarantee that the resulting closed-loop system is exponentially stable and achieves a prescribed disturbance attenuation level γ . Finally, a numerical example is provided to verify the effectiveness of the proposed method.
This work addresses the problem of developing a nonfragile sliding mode observer for fractional‐order complex networked systems (FO‐CNS) under stochastic network attacks. The proposed approach employs a combination of event‐triggered techniques. First, a nonfragile fractional‐order state observer is developed, enabling the design of a suitable sliding surface function. Next, a combination event‐triggered condition (CETC) is introduced, utilizing sampled error and sliding mode error vectors. For guaranteeing the stability of closed‐loop systems, sufficient conditions are derived by solving the linear matrix inequalities. Moreover, an improved self‐triggered condition is developed to avoid Zeno behavior. This condition relies on a predefined event‐triggered mechanism. The Gronwall–Bellman inequality is employed to determine a positive lower bound of the trigger sequence, ensuring the avoidance of infinite triggering within a finite time interval. Finally, two numerical simulations are provided to demonstrate the effectiveness and feasibility of the proposed method.
The ocean world is full of Unknown Marine Objects (UMOs), making it difficult to deal with unknown ocean targets using the traditional instance segmentation model. This is because the traditional instance segmentation networks are trained on a closed dataset, assuming that all detected objects are Known Marine Objects (KMOs). Consequently, traditional closed-set networks often misclassify UMOs as KMOs. To address this problem, this paper proposes a new open-set instance segmentation model for object instance segmentation in marine environments with UMOs. Specifically, we integrate two learning modules in the model, namely a prototype module and an unknown learning module. Through the learnable prototype, the prototype module improves the class’s compactness and boundary detection capabilities while also increasing the classification accuracy. Through the uncertainty of low probability samples, the unknown learning module forecasts the unknown probability. Experimental results illustrate that the proposed method has competitive known class recognition accuracy compared to existing instance segmentation models, and can accurately distinguish unknown targets.
The paper investigates the stability and passivity analysis problems for a class of uncertain neural networks with time-delay via delta operator approach. Both the parameter uncertainty and the generalized activation functions are considered in this paper. By constructing an appropriate Lyapunov-Krasovskii functional, some new stability and passivity conditions are obtained in terms of linear matrix inequalities (LMIs). The main characteristic of this paper is to obtain novel stability and passivity analysis criteria for uncertain neural networks with time-delay in the delta operator system framework. A numerical example is presented to demonstrate the effectiveness of the proposed results.
In this paper, the stability analysis and stabilization of a class of mode-dependent mean residence time-based discrete-time switching nonlinear systems are addressed. More specifically, under the mode-dependent average dwell time (MDADT) switching mode, combined with the practical problem, the nonlinear factor of nested actuator saturation (NAS) is introduced. Firstly, in order to ensure the system stability, based on the parameter-dependent discontinuous switching Lyapunov function and several basic lemmas, a state feedback controller is designed that makes the closed-loop system (CLS) achieve local exponential stability (LES) by solving the optimal problem in terms of linear matrix inequalities (LMIs). Secondly, the system considers the maximum attraction domain that can be achieved by the NAS system under the conditions. The simulation results show the effectiveness of the proposed design method. At the same time, the efficiency of the proposed method is verified via a water tank example.
In computer science (CS) academia it can be a challenge to help beginning students develop the thought process to be a successful software engineer. Although code is implemented in a linear manner, the mental construction and problem solving process is commonly nonlinear, requiring a high-level vision of class structures, control flow, etc., before any code is physically written. This concept can be difficult for beginning CS students to comprehend and use in their own coding projects. We provide a visualization that aims to help students more easily understand the coding thought process. This is accomplished by collecting keystroke data and incorporating it into an abstract syntax tree (AST), which creates a temporal AST. We provide the necessary information to prove that this visualization exists for any student's project where keystroke data is collected. We also refer to another type of keystroke visualization known as a Code Process Chart (CPC) that provided inspiration for the temporal AST. The goal is to eventually use these temporal ASTs alongside their corresponding CPCs to help students understand and improve their own coding thought process.
This paper is concerned with the problem of input-output finite-time stability (IO-FTS) for a class of discrete-time positive switched systems with time-varying delays. Two sufficient conditions for the existence of IO-FTS of such systems with respect to two different input signals are presented, respectively. All the results obtained are formulated in a set of linear inequalities. Two numerical examples are given to illustrate the effectiveness of the proposed results.
This brief investigates the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> fault estimation problem for a class of Lipschitz nonlinear systems with timevariant coefficient matrices in discrete-time settings. By introducing an auxiliary unknown input based on the nonlinear term, a quasi-linear model and its corresponding indefinite quadratic performance function for fault estimation are respectively given in lieu of the original nonlinear dynamics and the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance metric, such that the estimation problem is converted as an indefinite optimization problem. By artificially constructing a Krein-space based dynamic model, the classical linear estimation technique in H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sense is employed to seek a suitable choice of the estimation of the fault. A condition that ensures the existence of the estimator is derived analytically. A Kalman-filter-like estimator recursion is proposed simultaneously.
Considering that state distribution obeys a generalized Gaussian distribution, it cannot be accurately estimated using only the mean error criterion or mean square error criterion. Therefore, this brief addresses the high-order moment filter design problem for Markov jump systems in the finite frequency domain. An estimation method is applied by transforming the original stochastic system into a high-order component form via the cumulant generating function. Then, the generalized Kalman-Yakubovic-Popov lemma can be used in filter design, by considering finite frequency characteristics. Sufficient conditions for the existence of a high-order moment filter with the required finite frequency performance are established. Finally, a simulation example is presented to verify the effectiveness of the proposed method.
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.
In this paper, the universal adaptive control problem for a class of uncertain nonlinear systems is solved by the output feedback control approach. Firstly, a homogenous high-gain observer is proposed to estimate the system states based on the homogenous theory. Then, by using adding one power integrator method, a homogeneous controller is designed. It can be shown that all signals of the whole system are bounded and at the same time the system states globally asymptotically converge to the origin. In the end, we extend the proposed method to a class of upper-triangular nonlinear systems. Two examples are provided to illustrate the effectiveness of the proposed method.
