TY - GEN A1 - Rueda-Escobedo, Juan G. A1 - Moreno, Jaime A. A1 - Schiffer, Johannes T1 - L2-Gain Tuning for the Gradient Descent Algorithm in the Presence of Disturbances T2 - 2022 European Control Conference (ECC) N2 - Due to its simplicity and inexpensive computation, the gradient descent algorithm is one of the most used tools in adaptive control and system identification. Although it has been studied for decades, little has been achieved in terms of tuning methods in the presence of disturbances. One of the main difficulties in its analysis is the time-varying nature of the algorithm. In this work, we contribute in such direction by providing LMI tools for tuning the gradient descent algorithm gain such that a guaranteed upper bound on the L2 -gain with respect to parameter variations and measurement noise is achieved. Two academic examples are provided to illustrate the efficient application of the method. KW - Upper bound KW - Gain measurement KW - Minimization KW - robustness KW - system identification KW - noise measurement Y1 - 2022 UR - https://ieeexplore.ieee.org/document/9838051 SN - 978-3-9071-4407-7 U6 - https://doi.org/10.23919/ECC55457.2022.9838051 SP - 1610 EP - 1616 PB - IEEE CY - Piscataway ER -