Document Type
Technical Report
Subject Area
CPS Theory
Date of this Version
3-15-2016
Publication Source
Tech Report: Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization
Abstract
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
Keywords
Nonlinear systens, robust control, model predictive control, state constraints
Recommended Citation
Yash Vardhan Pant, Houssam Abbas, and Rahul Mangharam, "Tech Report: Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization", Tech Report: Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization . March 2016.
Date Posted: 15 March 2016