A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States | Zendy

Dalei Song | Zendy; Chong Wu | Zendy; Juntong Qi | Zendy; Jianda Han | Zendy; Tianran Wang | Zendy

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A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

Author(s) -

Dalei Song,

Chong Wu,

Juntong Qi,

Jianda Han,

Tianran Wang

Publication year - 2012

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/51904

Subject(s) - computer science , robustness (evolution) , nonlinear system , ellipsoid , filter (signal processing) , kernel adaptive filter , noise (video) , adaptive filter , nonlinear filter , set (abstract data type) , process (computing) , robot , control theory (sociology) , filter design , algorithm , artificial intelligence , computer vision , biochemistry , chemistry , physics , control (management) , quantum mechanics , astronomy , image (mathematics) , gene , programming language , operating system

The adaptive extended set‐membership filter (AESMF) for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method‐based adaptive set‐membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time‐varying states and parameters. As a result of using the proposed MIT‐AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method

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