Neural network based model predictive controller for simplified heave model of an unmanned helicopter

Authors:
Mahendra Kumar Samal;Sreenatha Anavatti;Matthew Garratt
Affiliations:
University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia;University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia;University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

Citing 4
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Abstract

Neural network (NN) based model predictive controller (NN-MPC) for height control of an unmanned helicopter is presented in this paper. The applicability of the NN-MPC scheme is evaluated on a simplified heave model of the helicopter in simulation. NN based system identification (NNID) technique is used to model the heave dynamics of the unmanned helicopter which is then used in the MPC algorithm to estimate the future control moves. To show the efficacy of the controller, controller results are provided. Results indicate that NN-MPC scheme is capable of handling external disturbances and parameter variations of the system.