TY - GEN
T1 - Active Noise Control for Harmonic and Broadband Disturbances Using RLS-Based Model Predictive Control
AU - Mohseni, Nima
AU - Nguyen, Tam W.
AU - Ul Islam, Syed Aseem
AU - Kolmanovsky, Ilya V.
AU - Bernstein, Dennis S.
N1 - Publisher Copyright:
© 2020 AACC.
PY - 2020/7
Y1 - 2020/7
N2 - This paper develops RLS-based MPC (RLSMPC), which uses multiple implementations of recursive least squares (RLS) to perform model predictive control (MPC). RLSMPC uses output-feedback measurements rather than full-state-feedback to construct the control input, thus removing the need for state estimation. To remove the need for an a priori model, RLSMPC uses RLS to perform online, closed-loop identification. This approach is applied to active noise control with unknown sinusoidal and broadband disturbances.
AB - This paper develops RLS-based MPC (RLSMPC), which uses multiple implementations of recursive least squares (RLS) to perform model predictive control (MPC). RLSMPC uses output-feedback measurements rather than full-state-feedback to construct the control input, thus removing the need for state estimation. To remove the need for an a priori model, RLSMPC uses RLS to perform online, closed-loop identification. This approach is applied to active noise control with unknown sinusoidal and broadband disturbances.
UR - http://www.scopus.com/inward/record.url?scp=85089571323&partnerID=8YFLogxK
U2 - 10.23919/ACC45564.2020.9147440
DO - 10.23919/ACC45564.2020.9147440
M3 - 会議への寄与
AN - SCOPUS:85089571323
T3 - Proceedings of the American Control Conference
SP - 1393
EP - 1398
BT - 2020 American Control Conference, ACC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 American Control Conference, ACC 2020
Y2 - 1 July 2020 through 3 July 2020
ER -