TY - GEN
T1 - Robust Adaptive MPC Using Uncertainty Compensation
AU - Tao, Ran
AU - Zhao, Pan
AU - Kolmanovsky, Ilya
AU - Hovakimyan, Naira
N1 - Publisher Copyright:
© 2024 AACC.
PY - 2024
Y1 - 2024
N2 - This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1 Ac) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1 AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework.
AB - This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1 Ac) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1 AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework.
KW - Adaptive control
KW - Constrained control
KW - Model predictive control
KW - Robust control
KW - Uncertainty compensation
UR - http://www.scopus.com/inward/record.url?scp=85204429416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204429416&partnerID=8YFLogxK
U2 - 10.23919/ACC60939.2024.10644611
DO - 10.23919/ACC60939.2024.10644611
M3 - Conference contribution
AN - SCOPUS:85204429416
T3 - Proceedings of the American Control Conference
SP - 1873
EP - 1878
BT - 2024 American Control Conference, ACC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 American Control Conference, ACC 2024
Y2 - 10 July 2024 through 12 July 2024
ER -