TY - GEN

T1 - A direct method for trajectory optimization using the particle swarm approach

AU - Ghosh, Pradipto

AU - Conway, Bruce A.

PY - 2011/10/6

Y1 - 2011/10/6

N2 - The particle swarm optimization (PSO) technique is utilized to solve a variety of trajectory optimization problems. PSO is a stochastic global optimization method which relies on a group of potential solutions to explore the search space. Conceptually, each particle in the swarm utilizes its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on the optimal solution. It is relatively easy to implement, and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although PSO has been successfully employed in solving discrete optimization problems, its application in dynamic optimization, as posed in the Optimal Control Theory, has been little explored. In this work, we use PSO to generate near-optimal solutions to several non-trivial trajectory optimization problems, including thrust programming for minimum-fuel, multi-burn coplanar rendezvous, and computing the maximum altitude climb path for an aircraft. The control functions are assumed to be linear combination of B-Splines, the coefficients of which are selected by the swarm optimizer. A dynamic multi-stage-assignment penalty function is incorporated to enforce the associated constraints. Finally, for each of the test cases considered, the PSO solution is compared with its gradient-based counterpart.

AB - The particle swarm optimization (PSO) technique is utilized to solve a variety of trajectory optimization problems. PSO is a stochastic global optimization method which relies on a group of potential solutions to explore the search space. Conceptually, each particle in the swarm utilizes its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on the optimal solution. It is relatively easy to implement, and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although PSO has been successfully employed in solving discrete optimization problems, its application in dynamic optimization, as posed in the Optimal Control Theory, has been little explored. In this work, we use PSO to generate near-optimal solutions to several non-trivial trajectory optimization problems, including thrust programming for minimum-fuel, multi-burn coplanar rendezvous, and computing the maximum altitude climb path for an aircraft. The control functions are assumed to be linear combination of B-Splines, the coefficients of which are selected by the swarm optimizer. A dynamic multi-stage-assignment penalty function is incorporated to enforce the associated constraints. Finally, for each of the test cases considered, the PSO solution is compared with its gradient-based counterpart.

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M3 - Conference contribution

AN - SCOPUS:80053423037

SN - 9780877035695

T3 - Advances in the Astronautical Sciences

SP - 775

EP - 794

BT - Spaceflight Mechanics 2011 - Advances in the Astronautical Sciences

T2 - 21st AAS/AIAA Space Flight Mechanics Meeting

Y2 - 13 February 2011 through 17 February 2011

ER -