Abstract

In this paper a controller design is proposed to get obstacle free trajectories in a three dimensional urban environment for unmanned air vehicles (UAVs). The controller has a two-layer architecture. In the upper layer, vision-inspired Grossberg neural network is proposed to get the shortest distance paths. In the bottom layer, a model predictive control (MPC) based controller is used to obtain dynamically feasible trajectories. Simulation results are presented for to demonstrate the potential of the approach.

Meeting Name

2006 American Control Conference

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Aerospace Control; Collision Avoidance; Control Engineering Computing; Controller Design; Model Predictive Control Based Controller; Neural Nets; Obstacle Avoidance; Obstacle Free Trajectories; Predictive Control; Remotely Operated Vehicles; Unmanned Air Vehicles; Vision-Inspired Grossberg Neural Network

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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