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.

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators. First, a fixed-time disturbance observer (FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems. Then an adaptive scheme is used and the adaptive FTDO (AFTDO) is developed, so that the priori knowledge of the lumped disturbance is not required. Further, a new non-singular fast terminal sliding mode (NFTSM) surface is designed by using an arctan function, which helps to overcome the singularity problem and enhance the robustness of the system. Based on the estimation of the lumped disturbance by the AFTDO, a fixed-time non-singular fast terminal sliding mode controller (FTNFTSMC) is developed to guarantee the trajectory tracking errors converge to zero within a fixed time. The settling time is independent of the initial state of the system. In addition, the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method. Finally, the fixed-time NFESM (FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers (SMCs) are performed. The comparative results confirm that the FTNFTSMC has superior control performance.

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly $(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$ -dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

This paper is concerned with event-based H∞ control for a class of networked Markov jump systems (MJSs) with missing measurements. The phenomenon of asynchronism occurs in both the controller and the actuator-failure model, which is estimated by the hidden Markov model, is taken into consideration. In addition, to reduce the burden of data transmission, a mode-dependent event-triggered mechanism (ETM) is proposed. Together with ETM, a network-induced delay is introduced. Subsequently, to guarantee that the MJS is stochastically stable, an event-based asynchronous controller is designed. Finally, to reveal the effectiveness of the proposed method, a simulation example of pulse width-modulation-driven boost converter is considered.

This paper investigates the state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities. Essentially different from most current literature, a novel switching law is developed to depict the dynamic behavior of switched memristive neural networks, in which the sojourn probabilities of each subsystem are assumed to be nonhomogeneous, and a higher-level deterministic switching signal is proposed to regulate proper feedback switching information by means of the average dwell time approach. Meanwhile, to alleviate the constraint network bandwidth resource efficiently, a dynamic event-triggered mechanism with a novel threshold parameter is proposed in determining if the current data should be released or not. By resorting to the Lyapunov functional technique and the stochastic analysis strategy, some sufficient conditions are addressed to ensure the stochastic stability of the augmented switched memristive neural networks. In the end, the effectiveness and superiority of the developed results are verified by a numerical example.