TY - GEN
T1 - Noncausal Lifting Linearization for Nonlinear Dynamic Systems Under Model Predictive Control
AU - Park, Seho
AU - Pangborn, Herschel C.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper presents a lifting linearization method for applying linear Model Predictive Control (MPC) to nonlinear dynamic systems. While existing lifting linearization methods provide accurate linear approximations when the nonlinearity is a function of the state only, they require additional assumptions or result in bilinear lifted representations when the nonlinearity is also a function of the control input. The proposed method approximates control-affine and control-nonaffine nonlinear dynamics with noncausal linear dynamics to achieve improved model accuracy. This noncausality in the lifted linear dynamics is then addressed within an MPC framework. Numerical examples illustrate that the proposed approach closely matches the performance of nonlinear MPC at a fraction of the computational cost, outpacing the performance of existing linearization methods.
AB - This paper presents a lifting linearization method for applying linear Model Predictive Control (MPC) to nonlinear dynamic systems. While existing lifting linearization methods provide accurate linear approximations when the nonlinearity is a function of the state only, they require additional assumptions or result in bilinear lifted representations when the nonlinearity is also a function of the control input. The proposed method approximates control-affine and control-nonaffine nonlinear dynamics with noncausal linear dynamics to achieve improved model accuracy. This noncausality in the lifted linear dynamics is then addressed within an MPC framework. Numerical examples illustrate that the proposed approach closely matches the performance of nonlinear MPC at a fraction of the computational cost, outpacing the performance of existing linearization methods.
UR - http://www.scopus.com/inward/record.url?scp=85146989027&partnerID=8YFLogxK
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U2 - 10.1109/CDC51059.2022.9993081
DO - 10.1109/CDC51059.2022.9993081
M3 - Conference contribution
AN - SCOPUS:85146989027
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1776
EP - 1781
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -