Paper Info
Title
Nonlinear Stochastic Receding Horizon Control: Stability, Robustness And Monte Carlo Methods For Control Approximation
Abstract
This work considers the stability of nonlinear stochastic receding horizon control when the optimal controller is only computed approximately. A number of general classes of controller approximation error are analysed including deterministic and probabilistic errors and even controller sample and hold errors. In each case, it is shown that the controller approximation errors do not accumulate (even over an infinite time frame) and the process converges exponentially fast to a small neighbourhood of the origin. In addition to this analysis, an approximation method for receding horizon optimal control is proposed based on Monte Carlo simulation. This method is derived via the Feynman-Kac formula which gives a stochastic interpretation for the solution of a Hamilton-Jacobi-Bellman equation associated with the true optimal controller. It is shown, and it is a prime motivation for this study, that this particular controller approximation method practically stabilises the underlying nonlinear process.
Year
DOI
Venue
2018
10.1080/00207179.2017.1349340
INTERNATIONAL JOURNAL OF CONTROL
Keywords
Field
DocType
Nonlinear stochastic receding horizon control, control approximation, Monte Carlo methods
Monte Carlo method,Mathematical optimization,Control theory,Nonlinear system,Optimal control,Control theory,Horizon,Robustness (computer science),Approximation error,Mathematics,Stochastic interpretation
Journal
Volume
Issue
ISSN
91
10
0020-7179
Citations 
PageRank 
References 
0
0.34
17
Authors
2
Name
Order
Citations
PageRank
francesco bertoli100.34
Adrian n. Bishop233425.08