Abstract
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.
Original language | English |
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Book series | IFAC-PapersOnLine |
Volume | 52 |
Issue number | 1 |
Pages (from-to) | 474-479 |
ISSN | 2405-8963 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | 12th IFAC Symposium on Dynamics and Control of Process Systems - Jurerê Beach Village Hotel, Florianópolis , Brazil Duration: 23 Apr 2019 → 26 Apr 2019 Conference number: 12 https://dycopscab2019.sites.ufsc.br/ |
Conference
Conference | 12th IFAC Symposium on Dynamics and Control of Process Systems |
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Number | 12 |
Location | Jurerê Beach Village Hotel |
Country/Territory | Brazil |
City | Florianópolis |
Period | 23/04/2019 → 26/04/2019 |
Internet address |
Keywords
- Nonlinear model predictive control
- Sequential quadratic programming
- Trust region algorithm