Work

Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators

Year: 2017

Type: article

Abstract: 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.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2017.2671343

Authors: Rushikesh Kamalapurkar, Benjamin Reish, Girish Chowdhary, Warren E. Dixon

Institutions: Oklahoma State University, University of Illinois Urbana-Champaign, University of Florida

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research

Awards: G4297280941, Adaptive dynamic programming for uncertain nonlinear systems through coupling of nonlinear analysis and data-based learning, G6911346901