TY - GEN
T1 - Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing
AU - Askari, Iman
AU - Zeng, Shen
AU - Fang, Huazhen
N1 - Funding Information:
2S. Zeng is with the Department of Electrical and System Engineering, Washington University, St. Louis, MO 63130, USA (e-mail: [email protected]) and was supported in part by the NSF grant CMMI-1933976.
Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to implement NMPC. We develop a constraint-aware particle filtering/smoothing method and exploit it to implement NMPC. The new sampling-based NMPC algorithm can be executed easily and efficiently even for complex nonlinear systems, while potentially mitigating the issues of computational complexity and local minima faced by numerical optimization in conventional studies. The effectiveness of the proposed algorithm is evaluated through a simulation study.
AB - Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to implement NMPC. We develop a constraint-aware particle filtering/smoothing method and exploit it to implement NMPC. The new sampling-based NMPC algorithm can be executed easily and efficiently even for complex nonlinear systems, while potentially mitigating the issues of computational complexity and local minima faced by numerical optimization in conventional studies. The effectiveness of the proposed algorithm is evaluated through a simulation study.
UR - http://www.scopus.com/inward/record.url?scp=85111916435&partnerID=8YFLogxK
U2 - 10.23919/ACC50511.2021.9482774
DO - 10.23919/ACC50511.2021.9482774
M3 - Conference contribution
AN - SCOPUS:85111916435
T3 - Proceedings of the American Control Conference
SP - 3532
EP - 3537
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -