Abstract | ||
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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. |
Year | DOI | Venue |
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2017 | 10.1016/j.sysconle.2017.06.005 | Systems & Control Letters |
Keywords | Field | DocType |
Optimal control,Model predictive control,Dissipativity | Dynamic programming,Mathematical optimization,Nonlinear system,Optimal control,Control theory,Model predictive control,End point,Control system,Economic cost,State space,Mathematics | Journal |
Volume | ISSN | Citations |
106 | 0167-6911 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Boris Houska | 1 | 214 | 26.14 |
Matthias A. Muller | 2 | 174 | 25.78 |