Neural dynamic optimization for autonomous aerial vehicle trajectory design

Peng Xu; Ajay Verma; Richard J. Mayer

doi:10.1117/12.720126

9 April 2007 Neural dynamic optimization for autonomous aerial vehicle trajectory design

Peng Xu, Ajay Verma, Richard J. Mayer

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 65760X (2007) https://doi.org/10.1117/12.720126
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Peng Xu, Ajay Verma, and Richard J. Mayer "Neural dynamic optimization for autonomous aerial vehicle trajectory design", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760X (9 April 2007); https://doi.org/10.1117/12.720126

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