UNKNOWN INPUT ESTIMATION FOR NONLINEAR SYSTEMS USING SLIDING MODE OBSERVERS AND SMOOTH WINDOW FUNCTIONS

被引：10

作者：

Chakrabarty, Ankush ^{[1
]}

Rundell, Ann E. ^{[2
]}

Zak, Stanislaw H. ^{[3
]}

Zhu, Fanglai ^{[4
]}

Buzzard, Gregery T. ^{[5
]}

机构：

[1] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA

[2] Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN 47907 USA

[3] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

[4] Tongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China

[5] Purdue Univ, Dept Math, W Lafayette, IN 47907 USA

来源：

SIAM JOURNAL ON CONTROL AND OPTIMIZATION | 2018年 / 56卷 / 05期

基金：

美国国家科学基金会;

关键词：

unknown input observers; low-pass filtering; incremental quadratic constraints; sliding mode; descriptor systems; multiplier matrix; linear matrix inequalities; UNOBSERVABLE DESCRIPTOR SYSTEMS; ROBUST FAULT RECONSTRUCTION; STATE ESTIMATION; MULTIVARIABLE SYSTEMS; LIPSCHITZ SYSTEMS; SENSOR FAULTS; DESIGN; CONTROLLERS; DIAGNOSIS; ACTUATOR;

D O I：

10.1137/16M1078793

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

While sliding mode observers (SMOs) using discontinuous relays are widely analyzed, most SMOs are implemented computationally using a continuous approximation of the discontinuous relays. This results in the formation of a boundary layer in a neighborhood of the sliding manifold in the observer error space. In this paper, a convex programming method for constructing boundary-layer SMOs (BL-SMOs) is presented for a wide class of nonlinear systems characterized by incremental quadratic constraints. Specifically, the observer gains are computed by solving a set of linear matrix inequalities and ultimate bounds on the state of a synthetic descriptor system. A novel application of filtering using smooth window functions is leveraged to reconstruct the unknown exogenous inputs to prescribed accuracy. Two numerical examples are presented to illustrate the effectiveness of our proposed approach.

引用

页码：3619 / 3641

页数：23

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