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This paper introduces an effective approach to study the stability of static neural networks with interval time-varying delay using delay partitioning approach and tighter integral inequality lemma. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, some suitable Lyapunov–Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the stability analysis issue for the considered system. These conditions are expressed in the framework of linear matrix inequalities, which heavily depend on the lower and upper bounds of the time-varying delay. It is shown, by comparing with existing approaches, that the delay-partitioning approach can largely reduce the conservatism of the stability results. Finally, three examples are given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents an autopilot design for a robotic unmanned surface vehicle in the presence of unknown yaw dynamics and measurement noises. A robust adaptive steering law is developed with the aid of a predictor, neural networks, and a modified dynamic surface control technique. Specifically, a predictor together with a low-frequency learning-based neural updating law is developed to identify the unknown yaw dynamics, as well as to reconstruct the states corrupted by measurement noises. Besides, to avoid the noise amplification effect of first-order filter, a linear tracking differentiator is incorporated into the dynamic surface control design approach to produce a noise-tolerant estimate of virtual control derivative. The stability of the closed-loop autopilot system is established by Lyapunov analysis. A salient feature of the developed controller is that it can achieve the desired performance in the presence of model uncertainty and measurement noises, simultaneously. Both simulation and experiment results are given to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]