The global asymptotic stabilization problem is investigated for a class of stochastic nonlinear time-varying delay systems under the weaker condition on functions. new parameter-dependent state and output feedback controllers are, respectively, proposed. Based time-delay system stability criterion, by tactfully introducing suitable Lyapunov-Krasovskii functional, globally asymptotically stable in probability closed-loop guaranteed rigorous proof. As practical application, model two-stage...

This paper addresses a distributed estimator design problem for linear systems deployed over sensor networks within multiple communication channels (MCCs) framework. A practical scenario is taken into account such that the channel used can be switched and switching governed by Markov chain. With existence of communicational imperfections external disturbances, an estimation algorithm proposed developed estimators are able to give accurate state estimates against phenomenon. The framework...

Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, sustainability. One typical control strategy for CAVs is the so-called cooperative adaptive cruise (CACC) where drive in platoons cooperate to achieve safe efficient transportation. In this study, we formulate CACC as a multi-agent reinforcement learning (MARL) problem. Diverging from existing MARL methods that use centralized training decentralized execution which...

This paper is concerned with a robust control problem of class networked systems operated within multiple communication channels (MCCs) environment. A practical scenario considered that the active channel in such MCCs for data switched and switching governed by Markov chain. For each channel, two network-induced imperfections, time delays, packet dropouts different characteristics are taken into account. Suppose plant subject to energy-bounded disturbance norm-bounded uncertainties,...

Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on detection are mainly developed based supervised approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming labor-intensive. As such, label-efficient methods, especially...

This note is concerned with the network-based resilient estimation problem. Multiple communication channels are considered to coexist in networked surroundings, contrast existing studies which only one channel used. In addition, it supposed that number of not fixed, consequently causes capability vary among a finite set modes. The regularity variation be modal persistent dwell time (MPDT) property. By constructing quasi-time-dependent (QTD) Lyapunov function, sufficient conditions on...

This paper is concerned with the problem of asynchronous H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering for a class discrete-time Takagi-Sugeno fuzzy affine systems against time-varying signal transmission delays and measurement quantization. The asynchrony refers to situation that plant state filter belong different local space regions, quantization density can be adjusted satisfy performance requirements at time instants....

This paper is concerned with the problem of robust filter design for a class discrete-time networked nonlinear systems. The Takagi-Sugeno fuzzy model employed to represent underlying dynamics. A multi-channel communication scheme that involves channel switching phenomenon described by Markov chain proposed data transmission. Two typical imperfections, network-induced time-varying delays and packet dropouts are considered in each channel. objective this an admissible such error system...

An appropriate subsystem configuration is a prerequisite for successful distributed control/state estimation design. Existing decomposition methods are not designed to handle simultaneous and control. In this article, we address the problem of general nonlinear process networks state control based on community structure detection. A systematic procedure modularity proposed. fast folding algorithm that approximately maximizes used in proposed find candidate configurations. Two chemical...

In this article, an event-triggered distributed state estimation mechanism is proposed for general linear systems that comprise several subsystems. Two moving horizon (MHE) algorithms can handle constraints on disturbances and noise are proposed. An event scheduler exploited to govern the evaluation of estimators networked information exchange between plant estimators, such good estimates be provided while both usage processors communication frequency reduced. The error by proven convergent...

In this work, we consider the problem of Koopman modeling and data-driven predictive control for a class uncertain nonlinear systems subject to time delays. A robust deep learning-based approach–deep recurrent operator is proposed. Without requiring knowledge system uncertainties or information on delays, proposed method able learn dynamics autonomously. framework established based operator. Conditions stability closed-loop are presented. The approach applied chemical process example....

One of the major obstacles along way electric vehicles' (EVs') wider global adoption is their limited driving range. Extreme cold or hot environments can further impact EV's range as a significant amount energy needed for cabin and battery temperature regulation while battery's power capacity are also impeded. To overcome this issue, we present an optimal control strategy based on nonlinear model predictive (NMPC) integrated thermal management (ITM) EVs, where proposed NMPC simultaneously...

Industrial predictive modeling, which provides valuable information for process monitoring and decision-making on operation, plays a crucial role in the industry. However, industrial processes commonly exhibit nonstationary characteristics caused by various drifts, such as frequent variations properties of raw materials. Hence, this article proposes an adaptive attention-driven manifold regularization (AAMR) strategy. Specifically, it designs working condition selection strategy to overcome...

Distributed state estimation plays a very important role in process control. Improper subsystem decomposition for distributed may increase the computational burdens, degrade performance, or even deteriorate observability of entire system. The problem nonlinear systems is investigated. A systematic procedure proposed. Key steps include test system, observable states identification each output measurement, relative degree analysis and sensitivity between measured outputs states. Considerations...