Robust MPC for constrained LPV systems with guaranteed ISS and finite L2-gain

The paper presents a robust model predictive control (MPC) algorithm for linear parameter-varying (LPV) systems subject to constraints on the state and control, and uncertain disturbance including model uncertainty and unknown but bounded disturbances. A finite horizon cost function on states, contr...

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Bibliographic Details
Published in2014 International Conference on Mechatronics and Control (ICMC) pp. 1136 - 1141
Main Authors Defeng He, Quanpeng Zhang, Lei Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
Closed loop systems
constraints
Cost function
Economic indicators
finite L 2 -gain
input-to-state stability
linear parameter varying systems
Model predictive control
Robustness
Stability analysis
Symmetric matrices
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Summary:The paper presents a robust model predictive control (MPC) algorithm for linear parameter-varying (LPV) systems subject to constraints on the state and control, and uncertain disturbance including model uncertainty and unknown but bounded disturbances. A finite horizon cost function on states, controls and disturbances is defined and the min-max optimization problem of robust MPC is reduced to a convex minimization problem involving linear matrix inequalities (LMI) constraints. In order to improve potential performance, a parameter-dependent MPC controller is developed so that it becomes self-scheduling via time-varying parameters measured in real-time. The notions of the input-to-state stability and finite L 2 -gain are introduced to achieve the closed-loop robustness of the obtained MPC controller. Finally, an example of classical angular positioning system is used to illustrate the effectiveness of results obtained here.
DOI:10.1109/ICMC.2014.7231730