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Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators
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DOI：10.1109/TAC.2017.2671343.png)
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Abstract
En 中文
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.
Keywords:
Adaptive systems
concurrent learning
Lyapunov methods
observers
parameter estimation
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