Design and stabilization of sampled-data neural-network-based control systems

This paper presents the design and stability analysis of sampled-data neural-network-based control systems. A continuous-time nonlinear plant and a sampled-data three-layer fully-connected feed-forward neural-network-based controller are connected in a closed-loop to perform a control task. Stability conditions would be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches would be employed to obtain the maximum sampling period and connection weights of the neural network subject to the considerations of the system stability and performance. An application example would be given to illustrate the design procedure and effectiveness of the proposed approach.

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