This paper is devoted to the simultaneous synchronization and input reconstruction in nonlinear discrete-time models. To deal with these, we propose an observer-based approach for two classes of dynamical systems that describe a large wide of nonlinear models. First, we consider the general case of nonlinear dynamics where the inputs to be reconstructed introduce nonlinearly into the model. The second class of systems considered here is composed of linear and nonlinear parts. Sufficient conditions for asymptotic convergence are derived from useful regular transformations. High performances, easiness of implementation, and robustness against disturbances will be shown through simulation results of some well-known nonlinear chaotic models.
机构:
School of Information Science and Engineering,Central South University
Hunan Engineering Laboratory for Advanced Control and Intelligent AutomationSchool of Information Science and Engineering,Central South University
机构:
Harbin Inst Technol, State Key Lab Robot & Syst HIT, Harbin 150006, Peoples R China
Australian Natl Univ, Res Sch Engn, Canberra, ACT 0200, AustraliaHarbin Inst Technol, State Key Lab Robot & Syst HIT, Harbin 150006, Peoples R China
Wang, Qingling
Yu, Changbin
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Shandong Comp Sci Ctr, Shandong Prov Key Lab Comp Networks, Jinan, Peoples R China
Australian Natl Univ, Res Sch Engn, Canberra, ACT 0200, AustraliaHarbin Inst Technol, State Key Lab Robot & Syst HIT, Harbin 150006, Peoples R China
Yu, Changbin
Gao, Huijun
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Harbin Inst Technol, State Key Lab Robot & Syst HIT, Harbin 150006, Peoples R China
KAU, Nonlinear Anal & Appl Math Grp NAAM, Jeddah, Saudi ArabiaHarbin Inst Technol, State Key Lab Robot & Syst HIT, Harbin 150006, Peoples R China
机构:
Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R ChinaUniv Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R China
Han, Dongkun
Chesi, Graziano
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Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R ChinaUniv Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R China