This article focuses on the robust exponential stabilization of positive uncertain switched neural networks subject to actuator saturation and sensor faults. Given the existence of interval uncertainty and the constraint concerning positivity of the original system, a new positive state-bounding observer is constructed to guarantee the coinstantaneous estimation of system state and sensor faults. To deal with actuator saturation, the convex hull scheme is employed. By designing the state-feedback controller and utilizing the multiple time-varying linear co-positive Lyapunov function, sufficient conditions for the robust exponential stability on the studied system are established under dwell-time switching. Furthermore, for optimizing the observer matrix, an iterative algorithm is developed. Eventually, a numerical example is exploited to illuminate the feasibility and effectiveness of both the deduced results and the proposed approaches.

本文重点研究受致动器饱和和传感器故障影响的正不确定切换神经网络的稳健指数稳定性。考虑到区间不确定性的存在和原系统正性的约束，构造了一个新的正状态边界观测器来保证系统状态和传感器故障的同步估计。为了处理致动器饱和，采用凸包方案。通过设计状态反馈控制器并利用多个时变线性共正李雅普诺夫函数，在驻留时间切换下建立了所研究系统的鲁棒指数稳定性的充分条件。此外，为了优化观察者矩阵，开发了一种迭代算法。最终，