期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 62, 期 7, 页码 3594-3601出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2671343
关键词
Adaptive systems; concurrent learning; Lyapunov methods; observers; parameter estimation
资金
- NSF [1509516]
- ONR [N00014-13-1-0151]
- AFOSR [FA9550-14-1-0399]
- AFRL, Munitions Directorate at Eglin AFB
A concurrent learning (CL)-based parameter estimator is developed to identify the unknown parameters in a nonlinear system. Unlike state-of-the-art CL techniques that assume knowledge of the state derivative or rely on numerical smoothing, CL is implemented using a dynamic state-derivative estimator. A novel purging algorithm is introduced to discard possibly erroneous data recorded during the transient phase for CL. Asymptotic convergence of the error states to the origin is established under a persistent excitation condition, and the error states are shown to be uniformly ultimately bounded under a finite excitation condition.
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