Details
| Original language | English |
|---|---|
| Pages (from-to) | 79-86 |
| Number of pages | 8 |
| Journal | Systems and Control Letters |
| Volume | 106 |
| Early online date | 14 Jul 2017 |
| Publication status | Published - 1 Aug 2017 |
| Externally published | Yes |
Abstract
This paper concerns a class of functions, named cost-to-travel functions, which find applications in model-based control. For a given (potentially nonlinear) control system, the cost-to-travel function associates with any given start and end point in the state space and any given travel duration the minimum economic cost of the associated point-to-point motion. Cost-to-travel functions are a generalization of cost-to-go functions, which are often used in the context of dynamic programming as well as model predictive control. We discuss the properties of cost-to-travel functions, their relations to existing concepts in control such as dissipativity, but also a variety of control-theoretic applications of this function class. In particular, we discuss how cost-to-travel functions can be used to analyze the properties of economic model predictive control with return constraints.
Keywords
- Dissipativity, Model predictive control, Optimal control
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
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In: Systems and Control Letters, Vol. 106, 01.08.2017, p. 79-86.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Cost-to-travel functions
T2 - A new perspective on optimal and model predictive control
AU - Houska, Boris
AU - Müller, Matthias A.
N1 - Funding information: The work of Boris Houska was supported by National Natural Science Foundation China (NSFC), No. 61473185, as well as ShanghaiTech University, Grant-No. F-0203-14-012. The work of Matthias A. Müller was supported by DFG Grant MU3929/1-1 and by the Baden-Württemberg Stiftung within the Eliteprogramme for Postdocs.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - This paper concerns a class of functions, named cost-to-travel functions, which find applications in model-based control. For a given (potentially nonlinear) control system, the cost-to-travel function associates with any given start and end point in the state space and any given travel duration the minimum economic cost of the associated point-to-point motion. Cost-to-travel functions are a generalization of cost-to-go functions, which are often used in the context of dynamic programming as well as model predictive control. We discuss the properties of cost-to-travel functions, their relations to existing concepts in control such as dissipativity, but also a variety of control-theoretic applications of this function class. In particular, we discuss how cost-to-travel functions can be used to analyze the properties of economic model predictive control with return constraints.
AB - This paper concerns a class of functions, named cost-to-travel functions, which find applications in model-based control. For a given (potentially nonlinear) control system, the cost-to-travel function associates with any given start and end point in the state space and any given travel duration the minimum economic cost of the associated point-to-point motion. Cost-to-travel functions are a generalization of cost-to-go functions, which are often used in the context of dynamic programming as well as model predictive control. We discuss the properties of cost-to-travel functions, their relations to existing concepts in control such as dissipativity, but also a variety of control-theoretic applications of this function class. In particular, we discuss how cost-to-travel functions can be used to analyze the properties of economic model predictive control with return constraints.
KW - Dissipativity
KW - Model predictive control
KW - Optimal control
UR - http://www.scopus.com/inward/record.url?scp=85023607580&partnerID=8YFLogxK
U2 - 10.1016/j.sysconle.2017.06.005
DO - 10.1016/j.sysconle.2017.06.005
M3 - Article
VL - 106
SP - 79
EP - 86
JO - Systems and Control Letters
JF - Systems and Control Letters
SN - 0167-6911
ER -