Lifted implicit integrators for direct optimal control

Abstract

Nonlinear Model Predictive Control (NMPC) relies on solving an Optimal Control Problem (OCP) online at every sampling time. The discretization of the continuous time dynamics requires the deployment of some numerical integration method. To that end, implicit integrators are often preferred when stiff or implicitly defined dynamics are present in the system. Implicit integration schemes, however, are typically more expensive to implement than explicit methods. This paper presents a novel lifting method for implicit integrators which improves their computational efficiency and accuracy in the context of Newton-type optimization algorithms. Similar to the standard lifted Newton, the proposed lifting method requires a marginal implementation effort. This novel approach has been implemented in the ACADO code generation software, and its efficiency illustrated using a nontrivial control example. An improved convergence and a computational speedup of about factor 2 are reported.