This paper investigates the finite-time event-triggered $\mathcal{H}_\infty$ control problem for Takagi-Sugeno Markov jump fuzzy systems. Because of the sampling behaviors and the effect of network environment, the premise variables considered in this paper are subject to asynchronous constraints. The aim of this work is to synthesize a controller via an event-triggered communication scheme such that not only the resulting closed-loop system is finite-time bounded and satisfies a prescribed $\mathcal{H}_\infty$ performance level, but also the communication burden is reduced. Firstly, a sufficient condition is established for the finite-time bounded $\mathcal{H}_\infty$ performance analysis of the closed-loop fuzzy system with fully considering the asynchronous premises. Then, based on the derived condition, the method of the desired controller design is presented. Two illustrative examples are finally presented to demonstrate the practicability and efficacy of the proposed method.

Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering H _∞ LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori, the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples.

This paper discusses the problem of composite $\mathcal {H}_{\infty }$ control for hidden Markov jump systems subject to replay attacks. Since it is difficult to obtain the mode information of the system directly in practice, a hidden Markov model is adopted in this paper to facilitate subsequent works. The hidden state represents the actual system dynamics that cannot be known exactly, but can be observed by the detector. Considering the multi-disturbance phenomenon, one of which is norm bounded and another is produced by an exogenous system, a composite $\mathcal {H}_{\infty }$ control scheme based on disturbance observer is designed to improve the anti-disturbance ability of the system. In addition, with the help of multi-sensor approach, a detection scheme, revealing the attacker's tactics and determining which sensor is assaulted, is presented to withstand replay attacks. Then, a composite disturbance observer-based controller, ensuring that the resulting system is stochastically stable with an expected $\mathcal {H}_{\infty }$ performance under replay attacks, is designed by solving convex optimization problems. Finally, the effectiveness and superiority of the developed method are verified by an example.

This paper studies the adaptive event-triggered fuzzy control issue for active vehicle suspension systems with uncertainties. Takagi-Sugeno fuzzy model is applied for considered systems. In the process of designing controller, a crucial problem, actuator failure, is taken into account. An adaptive event-triggered mechanism is adopted to economize limited communication resource. Compared with the traditional event-triggered scheme with a constant threshold, the adaptive event-triggered mechanism can save more resource effectively. Based on Lyapunov stability theory, an adaptive event-triggered fuzzy control approach is proposed to guarantee the desired performance. Meanwhile, suspension constrained requirements are also ensured. Finally, simulation examples are presented to testify the feasibility of the approach proposed in this paper.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the ``singularity'' problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.