This paper addresses the finite-time event-triggered control problem for nonlinear semi-Markovian switching cyber-physical systems (S-MSCPSs) under false data injection (FDI) attacks. Compared with the traditional time-triggered mechanism, the proposed event-triggered scheme (ETS) can effectively avoid network resource waste. Considering the network-induced delay in the modeling, a closed-loop system model with time delay is established in the unified framework. By the use of a mode-dependent piecewise Lyapunov-Krasovskii functional (LKF), stochastic finite-time stability (SFTS) criteria are established for the resultant closed-loop system. Then, some solvability conditions are established for the desired finite-time controller in light of a linear matrix inequality framework. Finally, an application example of vertical take-off and landing helicopter model (VTOLHM) is provided to demonstrate the effectiveness of the theoretical findings.

In this article, a novel reinforcement learning-based optimal tracking control (RLOTC) scheme is established for an unmanned surface vehicle (USV) in the presence of complex unknowns, including dead-zone input nonlinearities, system dynamics, and disturbances. To be specific, dead-zone nonlinearities are decoupled to be input-dependent sloped controls and unknown biases that are encapsulated into lumped unknowns within tracking error dynamics. Neural network (NN) approximators are further deployed to adaptively identify complex unknowns and facilitate a Hamilton-Jacobi-Bellman (HJB) equation that formulates optimal tracking. In order to derive a practically optimal solution, an actor-critic reinforcement learning framework is built by employing adaptive NN identifiers to recursively approximate the total optimal policy and cost function. Eventually, theoretical analysis shows that the entire RLOTC scheme can render tracking errors that converge to an arbitrarily small neighborhood of the origin, subject to optimal cost. Simulation results and comprehensive comparisons on a prototype USV demonstrate remarkable effectiveness and superiority.

In this paper, the formation control of unmanned surface vehicles (USVs) is addressed considering actuator saturation and unknown nonlinear items. The algorithm can be divided into two parts; one is steering the leader USV to trace along the desired path and the other is steering the follower USV to follow the leader in the desired formation. In the proposed formation control framework, a virtual USV is first constructed so that the leader USV can be guided to the desired path. To solve the input constraint problem, an auxiliary is introduced, and the adaptive fuzzy method is used to estimate the unknown nonlinear items in the USV. To keep the desired formation, the desired velocities of follower USVs are deduced by using geometry and Lyapunov stability theories. The stability of the closed-loop system is also proved. Finally, the effectiveness of the proposed approach is demonstrated by the simulation and experimental results.

The adaptive fuzzy finite-time tracking control problem of a class of switched nonlinear systems is investigated in this study. Fuzzy logic systems are introduced to handle the unknown nonlinear terms in the considered system. To overcome the drawback in the recursive design method, a finite-time command filter is employed. By constructing a new state-dependent switching law and adaptive fuzzy control signal, the existing restrictions on subsystems of switched systems are relaxed, all subsystems of the considered system are allowed to be unstabilizable. To avoid the Zeno behavior, a new hysteresis switching law is derived. It is proven that all states of the closed-loop system are bounded in finite time under the proposed fuzzy finite-time control scheme. Additionally, the proposed control method is extended to a class of more general switched large-scale nonlinear systems. Finally, two examples are provided to verify the developed method's effectiveness.

This paper considers the problem of sampled-data adaptive output feedback fuzzy stabilization for switched uncertain nonlinear systems associated with asynchronous switching. A state observer is designed to estimate the unmeasured states and fuzzy logic systems are employed to deal with the unknown nonlinear terms. Sampled-data controller and novel switched adaptive laws are constructed based on the recursive design method and an average dwell time constraint is given to ensure that the closed-loop system is stable. The proposed scheme is employed in a mass-spring-damper system to demonstrate its effectiveness.

This paper develops a new fuzzy dynamic outputfeedback control scheme for Takagi-Sugeno (T-S) fuzzy systems with time-varying input delay and output constraints based on (Q, S, R)-α-dissipativity. The proposed controller, called a (Q, S, R)-α-dissipative output-feedback fuzzy controller, takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for H∞ and passivity controllers as special cases for T-S fuzzy systems with time-varying input delay and output constraints. A dynamic parallel distributed compensator is used to design the (Q, S, R)-α-dissipative output-feedback fuzzy controller to ensure the asymptotic stability and strict (Q, S, R)-α-dissipativity of closed-loop systems described by a T-S fuzzy model that satisfies some output constraints. By employing the reciprocally convex approach, a new set of delay-dependent conditions for the desired controller is formulated in terms of the linear matrix inequality. The effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions.