Exploiting Convexity in Direct Optimal Control: A Sequential Convex Quadratic Programming Method
| Autor: | Moritz Diehl, Niels van Duijkeren, Rien Quirynen, Robin Verschueren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization 021103 operations research Optimization problem MathematicsofComputing_NUMERICALANALYSIS 0211 other engineering and technologies 02 engineering and technology Optimal control Convexity Inverted pendulum Local convergence 020901 industrial engineering & automation Algorithm design Quadratic programming Mathematics Sequential quadratic programming |
| Zdroj: | CDC |
| Popis: | Direct optimal control methods first discretize a continuous-time Optimal Control Problem (OCP) and then solve the resulting Nonlinear Program (NLP). Sequential Quadratic Programming (SQP) is a popular family of algorithms to solve this finite dimensional optimization problem. In the specific case of a least squares cost, the Generalized Gauss-Newton (GGN) method is a popular approach which works very well under some assumptions. This paper proposes a Sequential Convex Quadratic Programming (SCQP) scheme which exploits additional convexities in the NLP in order to generalize the GGN algorithm, possibly extend its applicability and improve its local convergence. These properties are studied in detail for the proposed SCQP algorithm, which will be compared to the classical GGN method using a numerical case study of the optimal control of an inverted pendulum. ispartof: pages:1099-1104 ispartof: Proceedings of the 2016 Conference on Decision and Control pages:1099-1104 ispartof: Conference on Decision and Control location:NV, Las Vegas date:12 Dec - 14 Dec 2016 status: published |
| Databáze: | OpenAIRE |
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