TY - GEN
T1 - Multi-objective optimal control of ascent trajectories for launch vehicles
AU - Ricciardi, Lorenzo A.
AU - Vasile, Massimiliano
AU - Toso, Federico
AU - Maddock, Christie Alisa
N1 - ©2016 AIAA
Ricciardi, L. A., Vasile, M., Toso, F., & Maddock, C. A. (2016). Multi-objective optimal control of ascent trajectories for launch vehicles. In AIAA/AAS Astrodynamics Specialist Conference, 2016. [2016-5669] (AIAA Space Forum). Reston, VA: American Institute of Aeronautics and Astronautics Inc, AIAA. DOI: 10.2514/6.2016-5669
PY - 2016/9/13
Y1 - 2016/9/13
N2 - This paper presents a novel approach to the solution of multi-objective optimal control problems. The proposed solution strategy is based on the integration of the Direct Finite Elements Transcription method, to transcribe dynamics and objectives, with a memetic strategy called Multi Agent Collaborative Search (MACS). The original multi-objective optimal control problem is reformulated as a bi-level nonlinear programming problem. In the outer level, handled by MACS, trial control vectors are generated and passed to the inner level, which enforces the solution feasibility. Solutions are then returned to the outer level to evaluate the feasibility of the corresponding objective functions, adding a penalty value in the case of infeasibility. An optional single level refinement is added to improve the ability of the scheme to converge to the Pareto front. The capabilities of the proposed approach will be demonstrated on the multi-objective optimisation of ascent trajectories of launch vehicles.
AB - This paper presents a novel approach to the solution of multi-objective optimal control problems. The proposed solution strategy is based on the integration of the Direct Finite Elements Transcription method, to transcribe dynamics and objectives, with a memetic strategy called Multi Agent Collaborative Search (MACS). The original multi-objective optimal control problem is reformulated as a bi-level nonlinear programming problem. In the outer level, handled by MACS, trial control vectors are generated and passed to the inner level, which enforces the solution feasibility. Solutions are then returned to the outer level to evaluate the feasibility of the corresponding objective functions, adding a penalty value in the case of infeasibility. An optional single level refinement is added to improve the ability of the scheme to converge to the Pareto front. The capabilities of the proposed approach will be demonstrated on the multi-objective optimisation of ascent trajectories of launch vehicles.
KW - multi-objective optimal control
KW - control problems
KW - solution strategies
KW - direct finite elements transcription method
KW - multi agent collaborative search
KW - ascent trajectories
KW - launch vehicles
UR - http://www.scopus.com/inward/record.url?scp=84995642075&partnerID=8YFLogxK
UR - http://www.space-flight.org/docs/2016_astro/2016_astro.html
U2 - 10.2514/6.2016-5669
DO - 10.2514/6.2016-5669
M3 - Conference contribution book
AN - SCOPUS:84995642075
SN - 9781624104459
T3 - AIAA Space Forum
BT - AIAA/AAS Astrodynamics Specialist Conference, 2016
CY - Reston, VA
T2 - AIAA/AAS Astrodynamics Specialist Conference, 2016
Y2 - 13 September 2016 through 16 September 2016
ER -