Robust exponential stabilization of positive uncertain switched neural networks with actuator saturation and sensor faults

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.