期刊
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
卷 358, 期 8, 页码 4209-4238出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2021.03.019
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资金
- National Natural Science Foundation of China [51705084]
- Natural Science Foundation of Guangdong Province [2018A030313999, 2019A1515011602]
- Fundamental Research Funds for the Central Universities [N2003032]
- Opening Project of Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology [2020kfkt05]
- University of Macau [MYRG2019-00028-FST]
This paper investigates the problem of non-fragile H-infinity control for uncertain active suspension systems with time-delay using a fuzzy control approach in the finite frequency domain. A T-S fuzzy model is constructed, and a sufficient condition is presented to ensure stability and desired performance simultaneously, which is further transformed into a convex optimization problem. Numerical simulations demonstrate the effectiveness and performance advantages of the proposed control approach.
In this paper, the problem of non-fragile H-infinity control for uncertain active suspension systems (ASSs) with time-delay is investigated via a fuzzy control approach in the finite frequency domain. Firstly, considering the variation of sprung and unsprung masses, a Takagi-Sugeno (T-S) fuzzy model is constructed to describe the nonlinear suspension dynamics based on a typical quarter-automobile ASS model. Meanwhile, the input delays and gain perturbations of the actuator are considered to approximate the real physical device situations in the control systems. Secondly, through combining Lyapunov stability theory, generalized Kalman-Yakubovich-Popov (GKYP) lemma with the further universalization of the strict S-procedure, a sufficient condition is presented to ensure that the resulted closed-loop system is asymp- totically stable and satisfies the desired finite frequency H(infinity )performance simultaneously. Furthermore, the existing conditions of the fuzzy controller are given as a convex optimization problem in terms of a set of linear matrix inequality (LMI) constraints. Finally, numerical simulations are implemented to examine the effectiveness and performance advantages of the proposed control approach. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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