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This paper addresses the problem of stochastic sampled-data stabilization for neural-network-based control systems (NNBCSs) with an optimal guaranteed cost. In order to stabilize the closed-loop system, continuous-time nonlinear plant and three-layer fully connected feed-forward neural networks based on stochastic sampling are connected to the closed loop. By introducing new Lyapunov–Krasovskii functional...
This paper deals with the problem of observer-based quantized control of nonlinear systems subject to actuator saturation and bounded disturbances. The nonlinearity is assumed to satisfy the local Lipschitz condition and appear in the state equation. Attention is focused on the design of an observer-based controller such that the resulting closed-loop system is convergent to a minimal ellipsoid for...
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