This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved by removing all the redundant variables, and thus the same feasible stabilization range can be obtained at the cost of more economical computational burden. Secondly, in order to further enhance the control efficiency, a dynamic trade-off between conservatism and complexity is established by proposing a new online evaluator of the updated system variation information across two adjacent sampling instants, and thus the so-called resilient stabilization is developed for reducing the conservatism without increasing or even alleviating the complexity. Moreover, all the determined matrices at the farthest sampling instant can be entirely removed without introducing any conservatism due to the proposed substitution technique. Finally, the superiority and effectiveness of our proposed methods are illustrated by a set of simulation comparisons with relevant research results reported in recent literature.

本文关注的是为离散时间 Takagi-Sugeno 模糊系统开发更有效的稳定条件的问题，即，不仅降低保守性，而且（同时）减轻模糊控制综合的复杂性。首先，在传统模糊镇定的框架下，通过去除所有冗余变量来改进最近文献报道的基于多重和的方法，从而以更经济的计算负担为代价获得相同的可行镇定范围。其次，为了进一步提高控制效率，通过提出一个新的跨两个相邻采样时刻更新系统变化信息的在线评估器，在保守性和复杂性之间建立了动态​​权衡，因此，所谓的弹性稳定是为了在不增加甚至减轻复杂性的情况下减少保守性而开发的。此外，由于所提出的替代技术，可以在不引入任何保守性的情况下完全去除最远采样时刻的所有确定矩阵。最后，我们提出的方法的优越性和有效性通过一组模拟比较与最近文献中报道的相关研究结果来说明。