Asian Journal of Control最新文献

In this paper, the $��{�H�}_{�\infty �}�$ control problem is investigated for the discrete-time interval type-2 T-S fuzzy system under the FlexRay protocol and relay channel. On the one hand, the FlexRay protocol is utilized to successfully alleviate the communication burden, which is a representative hybrid communication protocol. On the other hand, the introduction of relay channel can reduce the occurrence of channel fading phenomenon, which caused by long-distance wireless communication. This paper aims to design an $��{�H�}_{�\infty �}�$ output feedback controller to ensure the closed-loop interval type-2 T-S fuzzy system satisfies both exponential mean-square stability and $��{�H�}_{�\infty �}�$ performance. A sufficient condition for the existence of the fuzzy controller is derived through the definition of the Lyapunov stability. Finally, two simulation examples are utilized to demonstrate the validity of the suggested method.

Stabilization of classical nonlinear dynamical systems (with integer-order derivation) through boundary control is frequently studied in the literature. However, there are few works that have focused on this type of problems in the framework of fractional-order derivation and with time-delay. In this context, this work studies and presents a method for stabilizing a nonlinear dynamical system of PDE with time-delay via the Caputo–Hadamard fractional derivative (NSTDCH). In fact, the NSTDCH is a generalization of the classical nonlinear dynamical system of PDE. The method consists in designing robust Robin-type boundary controller for the practical stabilization of NSTDCH. Using some technical inequality, the properties of the Mittag–Leffler function and the Lyapunov method, some sufficient conditions for the practical stabilization of the NSTDCH by both right and left Robin boundary controls have been established. Also, by the same tools, we obtained sufficient conditions for the practical stabilization of the NSTDCH by distributed control. Note that the practical stabilization for NSTDCH through Robin boundary controls is novel. Finally, some numerical examples have been presented to show the effectiveness and robustness of Robin boundary controls for the practical stabilization of the NSTDCH.

This paper addresses the challenge of robust fast stabilization for a class of dynamical systems characterized by slow and ultra-slow dynamics. The system model is approximated using a distributed-order derivative of the measured output, which captures the influence of the control input, system dynamics, and external disturbances. The primary objective is to design a controller that ensures robust fast convergence while relying solely on limited structural knowledge of the system and available input-output data, without requiring direct measurement of the output signal derivatives. Two control strategies are presented: exponential stabilization and finite-time stabilization. Both approaches employ output feedback control, leveraging a high-order sliding mode observer based on the Levant non-recursive exact differentiator for estimating output derivatives and disturbances. The proposed framework ensures reliable state estimation and disturbance rejection, even under modeling uncertainties. Numerical simulations are conducted to validate the proposed methodology, demonstrating a superior performance in ensuring fast stabilization and robustness against disturbances. The results highlight the advantages of the finite-time stabilization approach in achieving fast convergence while maintaining robust control properties.

This article discusses the Mittag–Leffler synchronization (MLS) for generalized variable-order fractional (GVOF) neural networks with time delay via event triggering mechanism (ETM). First, a new GVOF inequality and stability theorem is derived, which extends existing results. Second, by employing ETM, a sufficient condition to obtain drive-response MLS of GVOF systems is presented. Ultimately, the Zeno behavior is eliminated. In addition, an example is established to test the derived results.

A new active fault diagnosis (AFD) method for stochastic distribution control (SDC) systems with bounded disturbance is proposed, and tracking control is performed using the information obtained from active fault diagnosis. Considering the actuator fault of the SDC system with non-Gaussian bounded disturbance and noise, the zonotope is used to describe the set of weight under different fault types. When the weight of the SDC system falls within a specific zonotope, the fault information of the system can be obtained and the tracking control can be carried out in time. Therefore, the primary objective of the AFD is to separate the zonotopes corresponding to different fault types. In this paper, the zonotopic box is introduced, and the distance from the center of the zonotopic box to its vertex is taken as the separation boundary of two or more zonotopes. For single-input systems, the paper provides an analytical solution for the auxiliary input. In the case of multi-input systems, the auxiliary input can be determined by solving a convex optimization problem. Finally, the algorithm is applied to a SDC system, and the effectiveness of the method is verified.

Extending beyond asymptotic stability dynamics of equilibrium points of nonlinear real-order systems remains a long-standing open problem because it is not known how to ensure asymptotic stability for some non-trivial bounded solutions. The design of controllers for nonlinear random initial-time real-order systems is known to give adequate conclusions in the absence of factual mathematical theory. We design a form of nonlinear control law strategy and introduce a novel concept to dynamic asymptotic stability that enables a deeper notion of convergence of non-trivial bounded solutions of such real-order systems. A governing dynamic equation associated with an external order that uses the knowledge of a given system and control function has been introduced. It is shown that under implementation of a form of nonlinear control law when the coefficient matrix of a dynamic equation satisfy matrix inequality obtained, the non-trivial bounded solutions are dynamic asymptotically stable and dynamic Mittag-Leffler asymptotically stable. We give three examples that include a practical model and numerical simulation demonstrations to illustrate reasonable evidence that the proposed method is effective.

By combining sliding mode control and adaptive control methods, distributed finite-time and fixed-time tracking consensus problems for multi-agent systems with uncertain disturbances are invested, where the leader has an external bounded input unknown to all followers. First, a new integral sliding mode function that has good robustness to bounded uncertain disturbances is designed to ensure that the system can reach the sliding mode surface in a fixed time. Second, based on the fact that system can reach the sliding mode surface, a distributed static consensus control protocol is proposed, guaranteeing that each follower can keep up with a time-varying leader with external bounded input in finite time. Third, in order to eliminate the limitation that control gains depend on certain global information, an adaptive finite-time protocol is further designed in a thoroughly distributed fashion. It is noteworthy that the above settling time depends on the initial states and may reach infinity as the initial states grow. Fourth, fixed-time control protocols are developed, guaranteeing that the settling time is bounded. Both static and adaptive cases are also explored. Theoretical analyses show that the proposed finite-time and fixed-time tracking consensus protocols are all feasible. Finally, four simulation examples are supplied to verify the effectiveness of the proposed protocols.

This paper is concerned with the issue of adaptive neural optimal dynamic surface control (DSC) for uncertain pure-feedback nonlinear systems with prescribed performance and unmodeled dynamics. A dynamic signal generated via an auxiliary linear system is employed to dispose of unmodeled dynamics. The adaptive prescribed performance tracking control is constructed by hyperbolic tangent function, which ensures that tracking error is in a specific area. The whole controller is composed of feedforward controller and feedback optimal controller. First, the feedforward controller is designed with the help of command filter and error compensating mechanism. In the meantime, the designed first-order filter in the traditional DSC is displaced by using the second-order stable linear systems. Second, by the aid of adaptive dynamic programming (ADP) method, neural networks (NNs) are utilized to estimate the optimal cost index and the optimal input. The updating laws of unknown neural network weights are constructed by gradient descent method and $��\sigma �$-modification approach. All the signals in the controlled system are proved to be semi-globally uniformly ultimately bounded (SGUUB) via theoretical analysis. Finally, the simulation curves of numerical examples verify the feasibility of the constructed control strategy.

This paper explores the stabilization of nonlinear neutral stochastic time-delay systems whose drift and diffusion coefficients are not subject to the linear growth conditions. The quantized feedback controller is designed using discrete-time mode and state observations instead of continuous-time observations to ensure the closed-loop system's $��{�H�}_{�\infty �}�$ stability, asymptotic stability, and exponential stability in $��{�L�}^{�\overline{�\rho �}�}�$. The discrete-time observations of state and mode are developed to save the cost of communication transmission in time, and the quantization is utilized to cut down on the difficulty of communication transmission under the large amount of information. In order to deal with the time delay, neutral term, and discrete-time observations in the system, the M-matrix principle and more specific Lyapunov function are constructed, and several stabilization criteria are also established. The presented results in this article are validated through a simulation example, which demonstrates their correctness and effectiveness.

This paper focuses on the problem of multi-agent resource allocation in the context of directed balanced network attacks and event-triggered communications. Considering the potential communication link failures due to external attacks, this paper proposes a continuous-time distributed resource allocation algorithm to effectively address these challenges. Additionally, an event-triggered communication mechanism is implemented, which intelligently determines the timing of information exchanges to enhance system responsiveness while significantly reducing communication resource consumption. Through extensive theoretical analysis, the algorithm is proven to effectively avoid Zeno behaviors and to converge exponentially to the optimal solution. Finally, some simulation experiments further validate the accuracy of the theoretical analysis and the practical effectiveness of the algorithm.