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This article discusses the issue of robust adaptive neural network (NN) consensus tracking control for nonlinear strict-feedback multi-agent systems with quantised input. By combining the neural network approach with robust techniques, a novel switching function is introduced to guarantee the tracking performance of this system. To estimate the unmeasured state, an NN-based adaptive state observer is developed. Based on backstepping dynamic surface control algorithms, a robust output feedback controller is constructed to guarantee that all signals in the closed-loop system remain globally uniformly ultimately bounded. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

In this article, a novel approach of a robust flight observer for a disturbance quadrotor transporting a payload in the presence of multiple time-varying delays is proposed. The developed algorithm is not only pertinent to a vehicle output signal with multiple small time-varying delays but also to long time-varying delays that are common for a quadrotor transporting a hanging load because a cascade structure is also employed to achieve outstanding performance, and the delay can be removed. The multiple time-varying delays and disturbances are compensated in the system with the proposed observer. The stability of the proposed methodology is proved using the Lyapunov–Krasovskii functional associated with an $ \mathcal {H}_\infty $ H ∞ criterion. Numerical simulations are shown to assess and validate the observer's performance and robustness under multiple time-varying delays and disturbances. [ABSTRACT FROM AUTHOR]