Indirect-adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty
Systems and Control
2015-09-25 v1 Optimization and Control
Abstract
We develop an indirect-adaptive model predictive control algorithm for uncertain linear systems subject to constraints. The system is modeled as a polytopic linear parameter varying system where the convex combination vector is constant but unknown. Robust constraint satisfaction is obtained by constraints enforcing a robust control invariant. The terminal cost and set are constructed from a parameter-dependent Lyapunov function and the associated control law. The proposed design ensures robust constraint satisfaction and recursive feasibility, is input-to-state stable with respect to the parameter estimation error and it only requires the online solution of quadratic programs.
Cite
@article{arxiv.1509.07170,
title = {Indirect-adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty},
author = {Stefano Di Cairano},
journal= {arXiv preprint arXiv:1509.07170},
year = {2015}
}