Conference paper · Journal article
Sequential l1 quadratic programming for nonlinear model predictive control
In this paper, we present and describe a computationally efficient sequential l1 quadratic programming (Sl1QP) algorithm for Nonlinear Model Predictive Control (NMPC). We use a tailored trust region sequential quadratic programming for the solution of the optimal control problem (OCP) involved in the NMPC algorithm.
We use a multiple shooting approach for numerical integration and sensitivity computation. A second order correction ensures a faster convergence of the SQP algorithm. We exploit the structure of the OCP by using an efficient primal-dual interior point algorithm based on Riccati factorizations and a block diagonal BFGS update of the Hessian matrix.
The complexity scales linearly with the prediction horizon length. We numerically evaluate and compare the performance of our algorithm on a numerical example.
| Language: | English |
|---|---|
| Year: | 2019 |
| Pages: | 474-479 |
| Proceedings: | 12th IFAC Symposium on Dynamics and Control of Process Systems |
| ISSN: | 24058963 and 24058971 |
| Types: | Conference paper and Journal article |
| DOI: | 10.1016/j.ifacol.2019.06.107 |
| ORCIDs: | Boiroux, Dimitri and Jørgensen, John Bagterp |
