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Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems
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DOI：10.1109/TNNLS.2014.2306202.png)
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Abstract
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This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.
Keywords:
Exponentially stable
neural networks
nonlinear systems
sampled-data control
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