Neuro-Optimal Control of Helicopter UAVs

Abstract

Helicopter UAVs can be extensively used for military missions as well as in civil operations, ranging from multi- role combat support and search and rescue, to border surveillance and forest fire monitoring. Helicopter UAVs are underactuated nonlinear mechanical systems with correspondingly challenging controller designs. This paper presents an optimal controller design for the regulation and vertical tracking of an underactuated helicopter using an adaptive critic neural network framework. The online approximator-based controller learns the infinite- horizoncontinuous-time Hamilton-Jacobi-Bellman(HJB) equation and then calculates the corresponding optimal control input that minimizes the HJB equation forward-in-time. In the proposed technique, optimal regulation and vertical tracking is accomplished by a single neural network (NN) with a second NN necessary for the virtual controller. Both of the NNs are tuned online using novel weight update laws. Simulation results are included to demonstrate the effectiveness of the proposed control design in hovering applications. (Unmanned Systems Technology XIII, edited by Douglas W. Gage, Charles M. Shoemaker, Robert E. Karlsen, Grant R. Gerhart)