Work

Non-linear gradient algorithm for parameter estimation

Year: 2015

Type: conference-paper

Abstract: Gradient algorithms are classical in adaptive control and parameter estimation. For instantaneous quadratic cost functions they lead to a linear time-varying dynamic system that converges exponentially under persistence of excitation conditions. In this paper we consider (instantaneous) non-quadratic cost functions, for which the gradient algorithm leads to non-linear (and non Lipschitz) time-varying dynamics, which are homogeneous in the state. We show that under persistence of excitation conditions they also converge globally, uniformly and asymptotically. Compared to the linear counterpart, they accelerate the convergence and can provide for finite-time or fixed-time stability.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc.2015.7402855

Authors: Juan G. Rueda-Escobedo, Jaime A. Moreno

Institution: Universidad Nacional Autónoma de México

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2230747754

FWCI: 0.934

Cites: 13

Cited by: 6

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