Document Type

Conference Paper

Subject Area

CPS Theory

Date of this Version


Publication Source

Conference on Decision and Control


For full version including proofs, see:

Y. V. Pant, H. Abbas, and R. Mangharam, “Tech report: Robust model predictive control for non-linear systems with input and state constraints via feedback linearization,” March 2016. [Online]. Available:


Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance.


Non-linear Control, Feedback Linearization, Predictive Control, Robust Control, Constrained System

Bib Tex

@INPROCEEDINGS{pantetal16cdc, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, title = {Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization}, booktitle = {Conference on Decision and Control}, year = {2016} }



Date Posted: 17 November 2016

This document has been peer reviewed.