Memory-based data-driven MRAC architecture ensuring parameter convergence
Convergence of controller parameters in standard model reference adaptive control (MRAC)
requires the system states to be persistently exciting (PE), a restrictive condition to be
verified online. A recent data-driven approach, concurrent learning, uses information-rich
past data concurrently with the standard parameter update laws to guarantee parameter
convergence without the need of the PE condition. This method guarantees exponential
convergence of both the tracking and the controller parameter estimation errors to zero …
requires the system states to be persistently exciting (PE), a restrictive condition to be
verified online. A recent data-driven approach, concurrent learning, uses information-rich
past data concurrently with the standard parameter update laws to guarantee parameter
convergence without the need of the PE condition. This method guarantees exponential
convergence of both the tracking and the controller parameter estimation errors to zero …
Memory-Based Data-Driven MRAC Architecture Ensuring Parameter Convergence
S Basu Roy, S Bhasin, I Narayan Kar - arXiv e-prints, 2016 - ui.adsabs.harvard.edu
Convergence of controller parameters in standard model reference adaptive control (MRAC)
requires the system states to be persistently exciting (PE), a restrictive condition to be
verified online. A recent data-driven approach, concurrent learning, uses information-rich
past data concurrently with the standard parameter update laws to guarantee parameter
convergence without the need of the PE condition. This method guarantees exponential
convergence of both the tracking and the controller parameter estimation errors to zero …
requires the system states to be persistently exciting (PE), a restrictive condition to be
verified online. A recent data-driven approach, concurrent learning, uses information-rich
past data concurrently with the standard parameter update laws to guarantee parameter
convergence without the need of the PE condition. This method guarantees exponential
convergence of both the tracking and the controller parameter estimation errors to zero …
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