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

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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. © 2012 Springer-Verlag.

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Samal, M. K., Anavatti, S., & Garratt, M. (2012). Neural network based model predictive controller for simplified heave model of an unmanned helicopter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7677 LNCS, pp. 356–363). https://doi.org/10.1007/978-3-642-35380-2_42

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