Multilevel approximate model predictive control and its application to autonomous vehicle active steering

  • Lee, Seung-Hi
  • Chung, Chung Choo
Citations

SCOPUS

23

초록

An innovative approximate explicit model predictive control strategy is proposed. A multilevel approximation scheme for state space partitioning is applied, which relies on an adaptive domain decomposition strategy using multidimensional tree techniques. Polytopes are generated from such state space partitioning, for which equivalent state feedback gains are computed such that approximate explicit controls can be simply computed. The proposed scheme requires no online optimization and thus computing control using pre-computed control gains is extremely fast. Through an application to autonomous vehicle lateral control, it is shown that the proposed method can achieve a significant improvement of computation time and approximation quality over other approximate predictive control methods.

키워드

Domain decomposition methodsModel predictive controlPartitions (building)Predictive control systemsState feedbackApproximate model predictive controlsApproximate predictive controlsApproximation qualityDecomposition strategyExplicit model predictive controlMulti-dimensional treesMultilevel approximationsOnline optimizationTrees (mathematics)
제목
Multilevel approximate model predictive control and its application to autonomous vehicle active steering
저자
Lee, Seung-HiChung, Chung Choo
DOI
10.1109/CDC.2013.6760795
발행일
2015-03
유형
Conference Paper
저널명
Proceedings of the IEEE Conference on Decision and Control
페이지
5746 ~ 5751