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Control of a Multivariable Lighting SystemHalldin, Axel January 2017 (has links)
This master’s thesis examines how a small MIMO lighting system can be identified and controlled. Two approaches are examined and compared; the first approach is a dynamic model using state space representation, where the system identification technique is Recursive Least Square, RLS, and the controller is an LQG controller; the second approach is a static model derived from the physical properties of light and a feedback feed-forward controller consisting of a PI controller coupled with a Control Allocation, CA, technique. For the studied system, the CA-PI approach significantly outperforms the LQG-RLS approach, which leads to the conclusion that the system’s static properties are predominant compared to the dynamic properties.
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