In this paper, distributed control of uncertain multi-agent systems (MAS) with completely unknown nonlinearities, unknown time-varying control coefficients and multiple unknown control directions is investigated. Firstly, a new theorem with a series of novel Nussbaum functions are presented, which solve the outstanding problem in control of nonlinear systems with unknown time-varying control coefficients and unknown signs. Secondly, global consensus of MAS with unknown time-varying control coefficients and unknown system nonlinearities under directed graph is firstly achieved, and the transient tracking performance is also guaranteed. Thirdly, a novel filter is proposed for each agent which does not require a prior knowledge of time derivative of leader. Finally, simulation results show the effectiveness of the proposed control schemes.

An adaptive fuzzy control approach is proposed for nonaffine pure-feedback systems with nonaffine functions being semibounded and possibly indifferentiable. A semibounded and continuous condition for nonaffine function is presented to guarantee the controllability of system. To overcome the difficulty from this mild condition, a compact set is introduced, and the nonaffine nonlinear functions are modeled in a new way based on this compact set, which is proved to be invariant set eventually. By using the dynamic surface control (DSC) technique, the problem of explosion of complexity inherent in backstepping method is avoided in the proposed adaptive fuzzy control scheme. Robust compensators are used to minify the influence of uncertainties and disturbances. Furthermore, it is proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

This work removes the critical assumptions of continuity, differentiability and state-independent boundedness which are typical of compounded disturbances in disturbance observer-based adaptive designs. Crucial in removing such assumptions are a novel observer-based design with state-dependent gain in place of a constant one, and a novel set-invariance design. The designs use different a priori knowledge of the disturbance, but they can both handle state-dependent (e.g. possibly unbounded) disturbances, as well as non-smooth (e.g. non-differentiable and jump discontinuous) disturbances. The tracking error is proven to be as small as desired by appropriately choosing design parameters. For the second design, which uses the least a priori knowledge of the disturbance, stability is proven by enhancing Lyapunov theory with an invariant-set mechanism, so as to construct an appropriate compact set resulting an invariant set for the closed-loop trajectories.

Amplitude constraints of Waverider Vehicles (WVs) will yield error fluctuations and further result in the singular problem of prescribed performance control (PPC), threatening the flight safety. It is the purpose of this study to propose a fuzzy-neural-approximation-based intelligent safety control method for WVs with actuator saturation. The saturation compensation is adopted by developing compensation systems to deal with the divergence of transformed error, which guarantees the realizability of prescribed performance. Moreover, performance functions are devised to construct a type of variable prescribed funnels which are actively readjusted based on actuator saturation. While the fragile problem associated with existing PPC is handled, and moreover quantitative prescribed performance indices are guaranteed for velocity tracking error and altitude tracking error of WVs. Besides, the addressed fuzzy-neural control does not require recursive design procedure of back-stepping and also avoids high computational burden. Finally, the superiority of design is validated via compared simulations.