Self-optimizing Robust Nonlinear Model Predictive Control

@inproceedings{Lazar2008SelfoptimizingRN,
  title={Self-optimizing Robust Nonlinear Model Predictive Control},
  author={Mircea Lazar and W. P. Maurice H. Heemels and Andrej Jokic},
  year={2008}
}
This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear systems that are affine in the control input and it allows for a decentralized implementation. 

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