An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 3253-3273 |
Journal / Publication | International Journal of Robust and Nonlinear Control |
Volume | 26 |
Issue number | 15 |
Publication status | Published - 1 Oct 2016 |
Link(s)
Abstract
An observer-based output feedback predictive control approach is proposed for linear parameter varying systems with norm-bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an H∞ -type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input-to-state stable observer, the closed-loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.
Research Area(s)
- LPV systems, model predictive control, output feedback, robust control
Citation Format(s)
An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances. / Yang, Weilin; Gao, Jianwei; Feng, Gang et al.
In: International Journal of Robust and Nonlinear Control, Vol. 26, No. 15, 01.10.2016, p. 3253-3273.
In: International Journal of Robust and Nonlinear Control, Vol. 26, No. 15, 01.10.2016, p. 3253-3273.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review