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System Identifcation and Optimal Control of an Autonomous Helicopter


A. Giger

Semester Thesis, HS14 (10388)

Uncertainties are present in several dynamical system models, either due to disturbances or due to modeling imperfections. This project develops an optimal controller for a remote controlled helicopter taking the uncertainties in the dynamics of the helicopter into account explicitly. For this purpose, statistical moments of the noise affecting the system were experimentally estimated using helicopter flight data. Given the stochastic model, the linear quadratic control problem was formulated for three different types of infnite horizon constraints; a secondary quadratic cost on the state and the input, a constraint on the discounted second order moment of the state and discounted chance constraints on the states. In all three cases, the objective was to maintain hover stability while minimizing control input. The resulting controllers were derived based on a semidefnite programming formulation of the constrained optimal control problem. The performance of the system was validated in simulation as well as implemented and tested on the helicopter platform RCopterX. It was also found that the performance of the stochastic controllers did not differ signifcantly from the case in which an optimal controller based on a deterministic model was used.

Supervisors: Maryam Kamgarpour, Tyler Summers, John Lygeros


Type of Publication:

(13)Semester/Bachelor Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5122
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