TY - GEN
T1 - A data-driven indirect method for nonlinear optimal control
AU - Tang, Gao
AU - Hauser, Kris
N1 - Funding Information:
This work was partially supported by NSF grant IIS-#1218534
Funding Information:
*This work was partially supported by NSF grant IIS-#1218534 1G. Tang is with department of Mechanical Engineering and Material Science, Duke University, Durham, NC 27708, USA gao.tang@duke.edu 2K. Hauser is with the Departments of Electrical and Computer Engineering and of Mechanical Engineering and Materials Science, Duke University, Durham, NC, 27708 USA kris.hauser@duke.edu
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality. This paper describes nearest-neighbors optimal control (NNOC), a data-driven framework for nonlinear optimal control using indirect methods. It determines initial guesses for new problems with the help of precomputed solutions to similar problems, retrieved using k-nearest neighbors. A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters. Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods, and sensitivity analysis can further reduce the solving time almost by half. Examples are shown on two optimal control problems in vehicle control.
AB - Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality. This paper describes nearest-neighbors optimal control (NNOC), a data-driven framework for nonlinear optimal control using indirect methods. It determines initial guesses for new problems with the help of precomputed solutions to similar problems, retrieved using k-nearest neighbors. A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters. Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods, and sensitivity analysis can further reduce the solving time almost by half. Examples are shown on two optimal control problems in vehicle control.
UR - http://www.scopus.com/inward/record.url?scp=85041951318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041951318&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206362
DO - 10.1109/IROS.2017.8206362
M3 - Conference contribution
AN - SCOPUS:85041951318
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4854
EP - 4861
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -