| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 708799 | IFAC-PapersOnLine | 2016 | 6 Pages |
Abstract
In this paper, we study a tracking control problem for linear time-invariant systems with model parametric uncertainties under input and states constraints. We apply the idea of modular design introduced in Benosman [2014], to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum seeking (ES) algorithm to iteratively learn the model uncertainties. The obtained MPC algorithms can be classified as iterative learning control (ILC)-MPC.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Anantharaman Subbaraman, Mouhacine Benosman,