Abstract

In this paper we outline some of the numerical heuristics used in existing sample-based MPC techniques and present a generic sample-based MPC algorithm for nonlinear optimal control. Compared to most of the existing techniques our generic algorithm does not place any restrictions on the form of the cost functions and dynamics used in the control problem formulation. We apply the numerical heuristics to the presented algorithm and compare their effectiveness individually by evaluating the control on an autonomous racing environment.

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