Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription

Ricciardi, Lorenzo A. and Vasile, Massimiliano and Maddock, Christie; (2016) Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription. In: 2016 IEEE Congress on Evolutionary Computation, (CEC). IEEE, CAN, pp. 869-876. ISBN 9781509006236 (https://doi.org/10.1109/CEC.2016.7743882)

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Abstract

This paper addresses the solution of optimal control problems with multiple and possibly conflicting objective functions. The solution strategy is based on the integration of Direct Finite Elements in Time (DFET) transcription into the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents performs a set of individual and social actions looking for the Pareto front. Direct Finite Elements in Time transcribe an optimal control problem into a constrained Non-linear Programming Problem (NLP) by collocating states and controls on spectral bases. MACS operates directly on the NLP problem and generates nearly-feasible trial solutions which are then submitted to a NLP solver. If the NLP solver converges to a feasible solution, an updated solution for the control parameters is returned to MACS, along with the corresponding value of the objective functions. Both the updated guess and the objective function values will be used by MACS to generate new trial solutions and converge, as uniformly as possible, to the Pareto front. To demonstrate the applicability of this strategy, the paper presents the solution of the multi-objective extensions of two well-known space related optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.

ORCID iDs

Ricciardi, Lorenzo A. ORCID logoORCID: https://orcid.org/0000-0002-0895-7961, Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465 and Maddock, Christie ORCID logoORCID: https://orcid.org/0000-0003-1079-4863;
  • Item type:Book Section
    ID code:59172
    Dates:
    Date
    Event
    21 November 2016
    Published
    15 March 2015
    Accepted
    Notes:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:optimal control, optimisation, aerospace engineering, space access, launch vehicle, finite elements in time (FET), collaboration, programming, Motor vehicles. Aeronautics. Astronautics, Mathematics, Artificial Intelligence, Modelling and Simulation, Computer Science Applications, Control and Optimization
    Subjects:Technology > Motor vehicles. Aeronautics. Astronautics
    Science > Mathematics
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Technology and Innovation Centre > Advanced Engineering and Manufacturing
    Depositing user: Pure Administrator
    Date deposited:20 Dec 2016 10:47
    Last modified:17 Dec 2021 01:20
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/59172
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