New Algorithms for Discrete-Time Parameter Estimation
| Autor: | Yingnan Cui, Joseph E. Gaudio, Anuradha M. Annaswamy |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Optimization and Control (math.OC) FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control Machine Learning (cs.LG) |
| DOI: | 10.48550/arxiv.2103.16653 |
| Popis: | We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under no PE condition. For the first algorithm, we show that in the presence of time-varying unknown parameters, the parameter estimation error converges uniformly to a compact set under conditions of persistent excitation, with the size of the compact set proportional to the time-variation of unknown parameters. Leveraging a projection operator, the second algorithm is shown to result in boundedness guarantees when the plant has constant unknown parameters. Simulations show better convergence results compared to recursive least squares (RLS) and comparable results to RLS with forgetting factor. Comment: 20 pages |
| Databáze: | OpenAIRE |
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