Neural dynamic optimization for autonomous aerial vehicle trajectory design
2007
Online aerial vehicle trajectory design and reshaping are crucial for a class of autonomous aerial vehicles such as
reusable launch vehicles in order to achieve flexibility in real-time flying operations. An aerial vehicle is modeled as a
nonlinear multi-input-multi-output (MIMO) system. The inputs include the control parameters and current system states
that include velocity and position coordinates of the vehicle. The outputs are the new system states. An ideal trajectory
control design system generates a series of control commands to achieve a desired trajectory under various disturbances
and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle.
Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system
state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these
difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control
parameters online. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP and
the control parameters will be used by the MIMO to generate new system states. By such a formulation, an NDO model
approximates the time-varying optimal feedback solution.
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