H∞ Performance Analysis of Sampled-data Systems with Polytopic Uncertainty via Sawtooth-characteristic-based Free-Matrix Integral Inequality*

This study investigates the
$H_{\infty}$
performance analysis of sampled-data systems (SDSs) with polytopic uncertainty. To better exploit the information on the sampling pattern, a sawtooth-characteristic-based free-matrix integral inequality and a second-order looped-functional are used to enhance the precision. Besides, to address the complex computation caused by high-order terms, the product of input delay and system states are also introduced as system variables. Then, stability criterion and the bounded real lemma for the
$H_{\infty}$
performance with less conservatism for the SDSs with polytopic uncertainty are yielded. Ultimately, the efficacy and advancement of the method are proved via a series of examples.