Automated Design of Multiphase Space Missions Using Hybrid Optimal Control

Chilan, Christian M.; Conway, Bruce A.

doi:10.2514/1.58766

Cited by 44 publications

(41 citation statements)

References 22 publications

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“…Some of them have finite duration such as a thrusting arc or a coasting arc, while others are instantaneous such as an impulsive maneuver [8]. The phase types can be grouped together into a categorical space Q of finite cardinality N Q ∈ N. The number of phases in a particular sequence is given by N s1 := N s + 1, where N s is the number of phase switches.…”

Section: Multiphase Optimal Control Problemmentioning

confidence: 99%

“…Chilan and Conway [8] and Englander et al [12] show the implementation of space mission automata using hybrid optimal control (HOC) solvers consisting of two nested loops [13]; an "outer-loop" finds the optimal mission structure, while the "inner-loop" finds the optimal continuous-time control history. If the inner-loop optimal trajectory solver cannot converge to a solution, the HOC solver will halt or find a sub-optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, the trajectory planner has had to resort to intuition and experience to generate such initial guesses. A recent approach has been to find an initial guess using a metaheuristic method such as a genetic algorithm (GA) [7][8][9] or particle swarm optimization (PSO) [10,11]. However, this does not ameliorate the problem entirely because the metaheuristic method solves a parameter optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Reachable Set Analysis Method for Generating Near-Optimal Trajectories of Constrained Multiphase Systems

Chilan

Conway

2014

J Optim Theory Appl

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Few sophisticated problems in the optimal control of a dynamical system can be solved analytically. There are many numerical solution methods, but most, especially those with the most potential accuracy, work iteratively and must be initialized with a guess of the solution. Satisfactory guesses may be very difficult to generate. In this work, a "Reachable Set Analysis" (RSA) method is developed to find nearoptimal trajectories for multiphase systems with no a priori knowledge. A multiphase system is a generalization of a dynamical system that includes possible changes on the governing equations throughout the trajectory; the traditional dynamical system where the governing equations do not change is included in the formulation as a special case. The RSA method is based on a combination of metaheuristic algorithms and nonlinear programming. A particularly beneficial aspect of the solution found using RSA is that it satisfies the system governing equations and comes arbitrarily close (to a degree chosen by the planner) to satisfying given terminal conditions. Three qualitatively different multiphase problems, such as a low-thrust transfer from Earth to Mars, a system with chattering arcs in the optimal control, and a motion planning problem with obstacles, are solved using the near-optimal trajectories found by RSA as initial guesses to show the effectiveness of the new method.

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Section: Multiphase Optimal Control Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Reachable Set Analysis Method for Generating Near-Optimal Trajectories of Constrained Multiphase Systems

Chilan

Conway

2014

J Optim Theory Appl

Self Cite

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“…Recently, hybrid optimal control (HOC) theory has been applied to the solution of space mission planning [11][12][13][14][15][16][17]. The common way to model a HOC problem consists of the steps of categorical state space modelling; continuous-time dynamics modelling; continuous-valued state and control spaces modeling; discrete events modelling; and cost functions modelling.…”

Section: Mathematical Description Of the Hoc Problemmentioning

confidence: 99%

“…Equation (12) describes that when the mission is completed, the remaining fuel of the SSc should be more than min fuel . Equation (13) represents the initial state constraint of the SSc.…”

Section: Summary Of the Hoc Problemmentioning

confidence: 99%

Optimal Scheduling of GEO On-orbit Refuelling with Uncertain Object Satellites

Huang

et al. 2017

MATEC Web Conf.

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Abstract. The mission planning of Geostationary Earth Orbit (GEO) on-orbit refuelling with uncertain object satellites is studied in this paper. Three key problems should be resolved here: Object Satellite (OSc) selection, mission sequence planning and transfer trajectory optimization. The optimization goal is to select the OSc and find the optimal servicing sequence with the best optimization objective value. Three optimization objectives, i.e. fuel cost, the total number of the OScs and the sum of the OSc priority value are taken into account. Considering this mission as a hybrid optimal control problem, a mathematical model is proposed. Multiobjective particle swarm optimization is employed to address the model. Several mission scenarios are compared and discussed. Numerical experiments indicate that 1) the method developed in this paper could address the problem efficiently; and 2) OSc distribution and varieties in optimization objective emphasis have considerable influences on the optimization results.

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Evolutionary and Heuristic Methods Applied to Problems in Optimal Control

Conway

2016

Variational Analysis and Aerospace Engineering

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Automated Design of Multiphase Space Missions Using Hybrid Optimal Control

Cited by 44 publications

References 22 publications

A Reachable Set Analysis Method for Generating Near-Optimal Trajectories of Constrained Multiphase Systems

A Reachable Set Analysis Method for Generating Near-Optimal Trajectories of Constrained Multiphase Systems

Optimal Scheduling of GEO On-orbit Refuelling with Uncertain Object Satellites

Evolutionary and Heuristic Methods Applied to Problems in Optimal Control

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