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The purpose of this paper is to devise a new approach to synthesize closed-loop feedback guidance law for online thrust-insensitive optimal trajectory generation utilizing neural networks.
The proposed methodology utilizes an open-loop variational formulation that initially determines optimal launch/ascent trajectories for various scenarios of known uncertainties in the thrust profile of typical solid propellant engines. These open-loop optimized trajectories will then provide the knowledge base needed for the subsequent training of a neural network. The trained network could eventually produce thrust-insensitive closed-loop optimal guidance laws and trajectories in flight.
The proposed neuro-optimal guidance scheme is effective for online closed-loop optimal path planning through some measurable and computable engine and flight parameters.
Determination of closed-loop optimal guidance law for non-linear dynamic systems with uncertainties in system and environment has been a challenge for researchers and engineers for many years. The problem of steering a solid propellant driven vehicle is one of these challenges. Even though a few researchers have worked in the area of non-linear optimal control and thrust-insensitive guidance, this paper proposes a new strategy for the determination of closed-loop online thrust insensitive guidance laws leading to optimal flight trajectories for solid propellant launch and ascent vehicles.
A neuro-optimal approach for thrust-insensitive trajectory planning
Aircraft Engineering and Aerospace Technology ; 81 , 3 ; 212-220
2009-05-15
9 pages
Article (Journal)
Electronic Resource
English
A neuro-optimal approach for thrust-insensitive trajectory planning
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