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The relaxation method of [1, Theorem 2.2] has been used in various studies to deal with parameterized linear matrix inequalities (PLMIs) without excessively increasing computational complexity. Inspired by this point, this paper proposes an improved relaxation method that can further reduce the conservatism of [1, Theorem 2.2] despite no additional slack variables, as well as reduce the number of LMIs (and LMI rows). To be specific, the reduction of both conservatism and computational complexity is verified through the well-known stabilization problems of both continuous-time and discrete-time T-S fuzzy systems, which shows the potential of the proposed method for its extension to other PLMI-based stabilization conditions.INDEX TERMS Slack variable-free relaxation method, parameterized linear matrix inequality, fuzzy systems, stabilization problem.• the conservatism of [1, Theorem 2.2] can be further reduced without employing additional slack variables

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This paper focuses on dealing with the problem of co-designing a fuzzy-basis-dependent event generator and an asynchronous filter of fuzzy Markovian jump systems via event-triggered non-parallel distribution compensation (non-PDC) scheme. The introduction of the event-triggered non-PDC scheme can reduce the number of real-time filter gain design operations with a large computational load. Furthermore, to perform an effective relaxation process, several kinds of time-varying parameters in filter design conditions are simultaneously relaxed by utilizing two zero equalities of transition probabilities and mismatch errors. In addition, to improve the considered performance, the event generation function is established based on fuzzy-basis-dependent event weighting matrices.

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