Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain
Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon.
|Title||Robust Control Design|
|Publisher||International Federation of Automatic Control|
|Conference||7th IFAC Symposium on Robust Control Design (ROCOND)|
|Period||20-06-12 → 22-06-12|
|Name||IFAC Proceedings Volumes (IFAC-PapersOnline)|
|Citations||Web of Science® Times Cited: No match on DOI|
- Control, Nonlinear systems, Optical radar, Scheduling, State space methods, Wind effects, Wind turbines, Robust control, LIDAR measurements, Linear parameter varying, Robust model predictive control, Lidar measurements, Linear parameter varying models, Minimax optimization, Prediction horizon, Scheduling variable, Special structure, State prediction, State space model, Wind speed
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