TY - GEN
T1 - Iterative optimal control synthesis for nonlinear switching systems
AU - Vu, Minh
AU - Zeng, Shen
N1 - Funding Information:
1Department of Electrical and System Engineering, Washington University in St. Louis, St. Louis, MO, USA, emails: {minhvu, s.zeng}@wustl.edu. This work was supported in part by the NSF grant CMMI-1933976.
Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - This paper introduces computational control solutions to the constrained optimal control problem for nonlinear switching systems. The proposed approaches enable direct incorporation of the system's full-scale dynamics and also allow the system to operate with multiple irregular (distinct) switches. The optimal control signal is synthesized by iteratively solving a sequence of linearly constrained quadratic programs. The effectiveness of the proposed framework is illustrated on the control synthesis of different legged robot models.
AB - This paper introduces computational control solutions to the constrained optimal control problem for nonlinear switching systems. The proposed approaches enable direct incorporation of the system's full-scale dynamics and also allow the system to operate with multiple irregular (distinct) switches. The optimal control signal is synthesized by iteratively solving a sequence of linearly constrained quadratic programs. The effectiveness of the proposed framework is illustrated on the control synthesis of different legged robot models.
UR - http://www.scopus.com/inward/record.url?scp=85111931542&partnerID=8YFLogxK
U2 - 10.23919/ACC50511.2021.9483002
DO - 10.23919/ACC50511.2021.9483002
M3 - Conference contribution
AN - SCOPUS:85111931542
T3 - Proceedings of the American Control Conference
SP - 998
EP - 1003
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -