2026-09-19T06:15:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/177532022-01-13T07:54:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David W. Carter.
Alaniz, Abran, 1980-
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2005-06-02T18:30:50Z
2005-06-02T18:30:50Z
2004
2004
http://hdl.handle.net/1721.1/17753
56523593
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.
Includes bibliographical references (p. 169-170).
Model Predictive Control (MPC) is a control strategy that is suitable for optimizing the performance of constrained systems. Constraints are present in all control systems due to the physical and environmental limits on plant operation. Through a systematical handling of constraints, MPC can improve the performance of a system by allowing it to safely operate near constraint boundaries. This thesis describes the mathematical background of MPC and develops two controllers. One controller is based on a linear model of the plant and is successfully applied to a real-time 3 degrees-of-freedom helicopter system, used to simulate helicopter-like motions in a laboratory setting. This system has a number of significant state and control constraints. The second controller uses a nonlinear model and is applied to a guided parafoil to identify the advantage of using a Doppler wind sensor. A method for reducing the computational load is also introduced that is applicable to both controllers.
by Abran Alaniz.
S.M.
170 p.
5403150 bytes
5402957 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Model predictive control with application to real-time hardware and guided parafoil
Thesis
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