Publications in Scientific Journals:
A.L. Dontchev, I. Kolmanovsky, D. Liao-McPherson, M. Nicotra, V.M. Veliov:
"Sensitivity-based Warmstarting for Constrained Model Predictive Control";
IEEE Transactions on Automatic Control,
65
(2020),
8;
4288
- 4294.
English abstract:
Model predictive control (MPC) is of increasing interest in applications for constrained control of multivariable systems. However, one of the major obstacles to its broader use is the computation time and effort required to solve a possibly non-convex optimal control problem (OCP) online. This paper introduces a
sensitivity-based warmstarting strategy for systems with nonlinear dynamics and polyhedral constraints with the goal of reducing the computational footprint of MPC controllers. It predicts changes in the solution of the parameterized OCP as the parameter varies, by calculating the semiderivative of the solution. We apply the theory of variational inequalities over polyhedral convex sets, thus avoiding restrictive conditions regarding the activity status of the constraints. A numerical study featuring MPC applied to unmanned aerial vehicles illustrates the proposed approach.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/TAC.2019.2954359
Created from the Publication Database of the Vienna University of Technology.