Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC). Issue 13 (2016)

• Record Type: Journal Article
• Authors: Subbaraman, Anantharaman; Benosman, Mouhacine
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 13(2016)
• Page Start: 193
• Page End: 198
• Publication Date: 2016
• Subjects: Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.07.950
• Languages: English
• ISSNs: 2405-8963

Abstract

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.