Numerical Methods for Efficient and Fast Nonlinear Model Predictive Control

The paper reports on recent progress in the real-time computation of constrained closed-loop optimal control, in particular the special case of nonlinear model predictive control, of large di.erential algebraic equations (DAE) systems arising e.g. from a MoL discretization of instationary PDE. Through a combination of a direct multiple shooting approach and an initial value embedding, a so-called “real-time iteration” approach has been developed in the last few years. One of the basic features is that in each iteration of the optimization process, new process data are being used. Through precomputation – as far as possible – of Hessian, gradients and QP factorizations the response time to perturbations of states and system parameters is minimized. We present and discuss new real-time algorithms for fast feasibility and optimality improvement that do not need to evaluate Jacobians online.