ICUAS'17 Paper Abstract


Paper WeA3.5

Pugach, Bogdan (Cal Poly Pomona), Beallo, Brian (California State Polytechnic University Pomona), Bement, David (Cal Poly Pomona), Brock, Justin (Cal Poly Pomona), Winterer, Kyle (California State Polytechnic University, Pomona), Rodriguez, Luis (California State Polytechnic University Pomona), Miller, Noah (California State Polytechnic University, Pomona), McGough, Sean (California Polytechnic State University Pomona), Bhandari, Subodh (Cal Poly Pomona), Aliyazicioglu, Zekeriya (Cal Poly Pomona), Sherman, Tristan (Cal Poly Pomona)

Nonlinear Controller for a UAV Using Echo State Network

Scheduled for presentation during the "Control Architecture - I" (WeA3), Wednesday, June 14, 2017, 11:20−11:40, Salon CD

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords Control Architectures, Autonomy, Simulation


A nonlinear adaptive controller for an unmanned aerial vehicle (UAV) has been developed using Echo State Network (ESN), which is a form of three-layered recurrent neural network (RNN). Online learning is used to train the ESN in real-time starting from randomized weights. The ESN is integrated into ArduPilot, an open source autopilot, for complex flight simulations. Software-in-the-loop and hardware-in-the-loop simulations are performed using the FlightGear Flight Simulator. The response of the UAV using the controller based on the ESN has surpassed the performance of the traditional controllers. Noise and external disturbances are added to show the effectiveness of the controllers. A UAV test platform is designed and built to gather aircraft flight data and test the ESN.



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