Model predictive control with tracking error bound and an influence function approach to moving horizon estimation
Model Predictive Control (MPC) and constrained Moving Horizon Estimation (MHE) are both optimization-based method where a constrained optimization problem is solved at each time instant. Recursive feasibility of the constrained optimization problem plays a key role in MPC as it ensures that the stat...
Main Author: | Zhou, Dexiang |
---|---|
Other Authors: | Ling Keck Voon |
Format: | Thesis |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/69439 |
Similar Items
-
Modelling and performance analysis of constrained GNSS vector tracking using moving horizon estimation
by: Wang, Yang
Published: (2016) -
Optimized dynamic policy for receding horizon control of linear time-varying systems with bounded disturbances
by: Gautam, A., et al.
Published: (2013) -
The detection bound of the probability of error in compressed sensing using Bayesian approach
by: Cao, Jiuwen, et al.
Published: (2013) -
Reset moving horizon estimation for quantized discrete time systems
by: Xu, Yong, et al.
Published: (2022) -
Linear set-membership state estimation with unknown but bounded disturbances
by: Foo, Y. K., et al.
Published: (2013)