This article explores a new strategy for designated time stabilization of stochastic time-varying nonlinear systems. Conventional prescribed-time stabilization has limitations in practical engineering due to singularities induced by infinite control amplitude and a lack of manipulation of the state response after a prescribed time. To address these challenges, we build a hybrid stabilization controller using state-scale techniques and a finite-time stabilization process that is bounded in probability over the full range, guaranteeing that the closed-loop system has a solution that is almost surely unique at a designated time. Compared to the current prescribed stabilization results, the proposed strategy not only ensures that the state of the system converges in probability to a compact set at a designated time and belongs to the set after which it eventually enjoys fast finite-time convergence. Finally, the effectiveness of the strategy is verified by simulating a real mass-spring mechanical system.

This research investigates the application of Integrated Sensing and Communication (ISAC) systems in maritime secure communication networks. Considering the severe path loss caused by complex maritime environments, we employ an active Reconfigurable Intelligent Surface (RIS) to establish supplementary communication links and enhance system performance. The primary objective is to maximize the sum secrecy rate (SSR) through the joint optimization of the hybrid precoding at the Dual-Function Base Station (DFBS) and the phase shift matrix of the active RIS, subject to both transmit power constraints and sensing performance requirements. We propose an alternating optimization algorithm based on fractional programming (FP) and successive convex approximation (SCA) techniques to solve this joint optimization problem. Numerical results demonstrate that the proposed algorithm achieves up to 10% SSR gain compared to passive RIS schemes, while maintaining excellent convergence performance.

This brief describes a fast finite time adaptive control strategy for p-norm stochastic nonlinear systems (SNSs) using adding a power integrator approach. In terms of convergence speed, finite time control (FTC) offers a substantial advantage in the vicinity of the equilibrium point, but it may exhibit notably slower convergence compared to exponential convergence when the initial state is far from the origin. To overcome this problem, the Lyapunov function is skillfully constructed in this study. A method is devised based on this function, which incorporates the characteristic of adding a power integrator technique and the symbolic function to effectively tackle challenges posed by complex system structures. Then, adaptive control is utilized to deal with the mismatched uncertain nonlinear function. Subsequently, a novel fast FTC strategy is proposed for p-norm SNSs with dead-zone via the back-stepping framework, which ensures the transient performance of the closed-loop system. In comparison to existing results, this controller effectively achieves performance control for p-norm SNSs with dead-zone and mismatched uncertainties functions. Finally, the superiority of the scheme is illustrated by comparative simulations. Note to Practitioners—The FTC problem is a prominent subject in the field of control, playing a crucial role in practical applications. This is especially notable in p-norm nonlinear systems, where the dynamic behavior exhibits uncontrollable linearization characteristics near the origin, introducing complexity to control analysis. Another challenging aspect in nonlinear control is the impact from external disturbances. In practical systems, the presence of random disturbances is inevitable and frequently results in system instability. A fundamental technical barrier is the involvement of Brownian motion integral terms in stochastic nonlinear systems, along with Itô differential, which introduces not only gradients in Lyapunov analysis but also the Hessian of the Lyapunov function. Furthermore, it is observed that mismatched uncertainties and dead-zone phenomena exist in real systems. Designing fast finite-time controllers has become a focal point of controller research. To address these issues, this paper proposes a fast FTC algorithm tailored for a class of p-norm SNSs with mismatched uncertainties. The proposed approach ensures that the system state can converge to the desired region near the origin in an almost fast finite time.

An adaptive neural networks event-triggered secure strategy is investigated to explore stochastic networked nonlinear systems with Denial-of-Service (DoS) attacks and unknown dead zone. In order to alleviate the negative effects of DoS attacks, a state estimator is constructed to approximate the immeasurable states. And the unknown dead zone input function is described as a bounded disturbance and a time-varying nonlinear function to offset the unknown dead zone effect. To defend against DoS attacks while ensuring system stability, an adaptive event-triggered prescribed performance controller is designed, ensuring that all signals of the closed-loop system are bounded in probability, and the tracking error tends to the designed performance bound within a predefined finite time. Meanwhile, this secure strategy can completely eliminate potential Zeno behavior. Subsequently, the modified average dwell time (ADT) approach was integrated with Lyapunov stability theory to establish the stability of the system. Eventually, two simulation results are utilized to prove the effectiveness of the developed strategy.

This article explores the tracking issue for dc-dc buck power converter with stochastic disturbance, specifically focusing on how output voltage tracks to a desired voltage in a finite-time when the load changes. Meanwhile, considering unmodeled dynamics and nonaffine inputs, we propose an innovative finite-time fuzzy prescribed performance switching tracking algorithm to achieve tracking goal. For realizing the requirements of performance, the algorithm converts the tracking error to a new state by means of a coordinate transformation via the tangent function. In addition, the universal approximation capacity of the fuzzy-logic system is utilized to estimate the unknown nonlinear term effectively. On this basis, the designed adaptive dynamic event-triggered controller can not only ensure that all the signals for the closed-loop system remain bounded in probability but also guarantee that the tracking error will converge to a predetermined small neighborhood. Meanwhile, different piecewise functions are added into the controller to characterize prescribed performance and avoid singularity problems, respectively. Finally, the effectiveness of the tracking control algorithm is fully demonstrated by the simulations of the dc-dc buck power converter.

This article extends the finite-time adaptive tracking control to stochastic nonlinear systems with multiple uncertainties, including output constraints, unknown parameters, unmodeled dynamics, and external disturbances. Most relevant results in literature have two main restrictions: 1) uncertainties and unknown items in the systems; 2) the matter of “explosion of complexity.” By integrating an improved technique of adding fuzzy logic systems to estimate unknown parameters with a bounded command filter method, a systematic tracking control scheme is developed that eliminates both of the above restrictions. To alleviate the serious uncertainties caused by the constraints, a quartic asymmetric time-varying barrier Lyapunov function is utilized when the control coefficient is known. Subsequently, an event-triggered controller is constructed that enables boundedness and finite-time convergence of all closed-loop signals. Eventually, to validate the effectiveness of the proposed adaptation strategy, this new method is applied to ship maneuvering systems that encounters multiple uncertainties arising from disturbances, such as wind, waves, and ocean currents.

A fast finite-time adaptive fuzzy tracking control algorithm is proposed for stochastic nonlinear systems (SNSs) under an event-triggered mechanism. Unlike the traditional finite-time control of SNSs, for this article, the drift and diffusion terms can be completely unknown. First, a fuzzy-logic system has been implemented to approximate the uncertain functions of SNSs. Second, a novel theorem of a fast finite-time adaptive control mechanism of deterministic systems is presented by revamping the fast finite-time stability in the mean square. Next, the complex explosion issue caused by the backstepping technique is effectively avoided based on command filtering feedback control. Compared with the standard backstepping technique, which has avoided the analytical computations of the derivatives of virtual control functions and has dramatically reduced the computational burden. Finally, an example of the one-link manipulator with motor dynamic systems is provided to verify the theoretical analysis.

In recent years, 5G/5G-A technology has fast developed and found widespread deployment, meeting the diverse requirements of application scenarios across various industries. In this paper, we introduce the principle and advantages of 5G/5G-A private network. Then, we introduce the construction of 5G/5G-A private network. Furthermore, we design an intelligent operation system of 5G/5G-A private network, which includes six key modules with over twenty functionalities. This intelligent operation system can effectively support the operation of 5G/5G-A private network. Lastly, this paper introduces the 5G/5G-A private network applications in a realistic vehicle factory.

As a key component of human-computer intelligent interaction and many real-world applications, the real-time property of facial expression recognition is especially important. However, the recognition result of conventional video-based approaches can not be given until the entire video is finished. In this letter, we deal with early expression detection, which aims to identify the expression as early as possible before its ending. This is a relatively new and challenging problem. Max-margin early event detector (MMED) is a well-known framework, which can make early detection. However, the linearity restricts its applications. We thus introduce kernel learning to model the nonlinear structure of complex data distribution. Moreover, the model is further reformulated in an online setting to address the streaming videos. The high retraining cost and large memory requirement of MMED are thus significantly reduced. In addition, we employ AlexNet architecture to make further comparison with mid-level features. Experiments on two popular video-based expression datasets demonstrate both the effectiveness and efficiency of the proposed method.

The existence of singularities often affects the learning dynamics in feedforward neural networks. In this paper, based on theoretical analysis results, we numerically analyze the learning dynamics of radial basis function (RBF) networks near singularities to understand to what extent singularities influence the learning dynamics. First, we show the explicit expression of the Fisher information matrix for RBF networks. Second, we demonstrate through numerical simulations that the singularities have a significant impact on the learning dynamics of RBF networks. Our results show that overlap singularities mainly have influence on the low dimensional RBF networks and elimination singularities have a more significant impact to the learning processes than overlap singularities in both low and high dimensional RBF networks, whereas the plateau phenomena are mainly caused by the elimination singularities. The results can also be the foundation to investigate the singular learning dynamics in deep feedforward neural networks.

Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation.