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Fast Model Predictive Control of Miniature Coaxial Helicopters


Konstantin Kunz

Master Thesis, FS 12 (10184)

Model Predictive Control (MPC) is a well developed automatic control technology, that computes a control strategy by solving a convex optimization problem at every sampling period. Traditionally, MPC is used to control slow processes present in chemical or cement industries because the computation of the optimal solution is time consuming. Recent developments in fast convex optimization solvers, that can be tailored to specific optimization problems, allow the use of MPC in fast processes such as the real-time control of a miniature coaxial helicopter. In this work we present a successful implementation of a MPC automatic control approach for miniature remote controlled coaxial helicopters. The nonlinear dynamic behavior of the helicopter was identified, simplified and approximated by a Linear Time Varying (LTV) model. The convex optimization solver was generated for the specific convex problem and integrated into a MPC control algorithm. It can be shown that the LTV model improves performance compared to a simple linearized model. Furthermore, the use of integral states in the model is justified and performance analysis show that the MPC approach performs better than a tuned Proportional Integral Differential (PID) controller and a Approximate Dynamic Programming (ADP) controller.


Type of Publication:

(12)Diploma/Master Thesis

S. M. Huck

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2012:IFA_4228
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