13,565 research outputs found
Preconditioned Continuation Model Predictive Control
Model predictive control (MPC) anticipates future events to take appropriate
control actions. Nonlinear MPC (NMPC) describes systems with nonlinear models
and/or constraints. A Continuation/GMRES Method for NMPC, suggested by T.
Ohtsuka in 2004, uses the GMRES iterative algorithm to solve a forward
difference approximation of the Continuation NMPC (CNMPC) equations on
every time step. The coefficient matrix of the linear system is often
ill-conditioned, resulting in poor GMRES convergence, slowing down the on-line
computation of the control by CNMPC, and reducing control quality. We adopt
CNMPC for challenging minimum-time problems, and improve performance by
introducing efficient preconditioning, utilizing parallel computing, and
substituting MINRES for GMRES.Comment: 8 pages, 6 figures. To appear in Proceedings SIAM Conference on
Control and Its Applications, July 8-10, 2015, Paris, Franc
Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
This paper proposes an algorithmic framework for solving parametric
optimization problems which we call adjoint-based predictor-corrector
sequential convex programming. After presenting the algorithm, we prove a
contraction estimate that guarantees the tracking performance of the algorithm.
Two variants of this algorithm are investigated. The first one can be used to
solve nonlinear programming problems while the second variant is aimed to treat
online parametric nonlinear programming problems. The local convergence of
these variants is proved. An application to a large-scale benchmark problem
that originates from nonlinear model predictive control of a hydro power plant
is implemented to examine the performance of the algorithms.Comment: This manuscript consists of 25 pages and 7 figure
Sparse preconditioning for model predictive control
We propose fast O(N) preconditioning, where N is the number of gridpoints on
the prediction horizon, for iterative solution of (non)-linear systems
appearing in model predictive control methods such as forward-difference
Newton-Krylov methods. The Continuation/GMRES method for nonlinear model
predictive control, suggested by T. Ohtsuka in 2004, is a specific application
of the Newton-Krylov method, which uses the GMRES iterative algorithm to solve
a forward difference approximation of the optimality equations on every time
step.Comment: 6 pages, 5 figures, to appear in proceedings of the American Control
Conference 2016, July 6-8, Boston, MA, USA. arXiv admin note: text overlap
with arXiv:1509.0286
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