This paper presents a novel approach on Model Predictive Control (MPC) using an inhomogeneously discretized preview horizon for the application of urban energy efficient driving. One solution for model predictive energy efficient driving is a direct solution of the underlying speed profile optimization problem using Quadratic Programming (QP), which allows computationally efficient and robust results. Our inhomogeneous horizon discretization allows to have a finer discretization of the typically important near future and a wider discretization of the less decisive far range of an MPC, while keeping a long preview horizon and at the same time limit the number of supporting points, hence limit the problem dimension, computational complexity, and proportional execution time. In extensive simulations of a real-world urban driving scenario, we demonstrate a significantly improved control performance in terms of fuel consumption, trip time, or constraint violation for the same computational complexity.
Inhomogeneous Model Predictive Control Horizon Discretization for an Urban Truck Energy Efficient Driving Application
2015-09-01
227512 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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AIAA | 2014
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