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Observer-based Sampled-data Robust Model Predictive Control for Linear Parameter Varying Systems With Saturated Control Input
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DOI：10.1007/s12555-024-1202-8.png)
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Abstract
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This paper investigates an observer-based sampled-data robust model predictive control (MPC) approach for continuous-time linear parameter varying (LPV) systems with bounded disturbances. The state observer system is designed to estimate unknown system states, and the state observer gain is off-line optimized to ensure that the estimation errors are constrained within time-varying ellipsoidal sets. The on-line MPC optimization guarantees robust stability of the augmented closed-loop system by synthesizing the off-line state observer gain and on-line feedback controller gain. The conservativeness of the control input is reduced by considering the saturation nonlinearity via the convexity combinations of the actual feedback controller and auxiliary feedback controller. The conditions on the robust MPC optimization are formulated as linear matrix inequalities. The robust MPC optimization problem has recursive feasibility and can guarantee robust stability of the continuous-time LPV systems.
Keywords:
Linear parameter varying system
model predictive control
robust control
sampled-data control
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