Tuesday, 13:45 - 14:10 h, Room: H 0112


Christian Kirches
A real-time iteration scheme for mixed-integer nonlinear model predictive control

Coauthors: Hans-Georg Bock, Sebastian Sager


A class of nonlinear model predictive control problems with both continuous and binary controls is considered. Partial outer convexification and relaxation is used to obtain a continuous model predictive control problem with possibly increased control dimension. The problem can then be solved by combining a direct method for optimal control with a rounding scheme. Feasibility and comptimality certificates hold, while numerical computations typically do not
involve an exponential runtime effort. It is argued that the idea of real-time iterations proposed by Diehl et al. can be used to devise a new mixed-integer real-time iteration scheme for this problem class. To this end, it is shown that adding a rounding step to one iteration of the scheme can be interpreted as carrying out a step of an perturbed Newton-type method. Sufficient conditions for local contractivity of such a perturbed method are derived. Based on this local contractivity argument, a proof of locally asymptotic convergence of the proposed scheme on a receeding horizon is given for the nonlinear discrete-time case. An upper bound on the allowable sampling time of the scheme and on the loss of optimality is derived.


Talk 2 of the invited session Tue.2.H 0112
"Real-time optimization II" [...]
Cluster 16
"Nonlinear programming" [...]


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