TY - GEN
T1 - Robust MPC for Nonholonomic Robots with Moving Obstacle Avoidance
AU - Hao, Yanye
AU - Dai, Li
AU - Xie, Huahui
AU - Guo, Yongzhen
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/9
Y1 - 2020/10/9
N2 - In this paper, we propose a robust model predictive control algorithm for discrete-time nonholonomic robot systems with additive disturbances. To achieve moving obstacle avoidance, the related polyhedral over-approximations are utilized to realize the reformulation of obstacle avoidance constraint. Thus, the resulting model predictive control optimization problem can be solved effectively by standard nonlinear programming solvers. Moreover, the theoretical guarantees for recursive feasibility and input-to-state stability are provided. Finally, the efficiency of the proposed algorithm is verified by the simulation results.
AB - In this paper, we propose a robust model predictive control algorithm for discrete-time nonholonomic robot systems with additive disturbances. To achieve moving obstacle avoidance, the related polyhedral over-approximations are utilized to realize the reformulation of obstacle avoidance constraint. Thus, the resulting model predictive control optimization problem can be solved effectively by standard nonlinear programming solvers. Moreover, the theoretical guarantees for recursive feasibility and input-to-state stability are provided. Finally, the efficiency of the proposed algorithm is verified by the simulation results.
UR - http://www.scopus.com/inward/record.url?scp=85098053321&partnerID=8YFLogxK
U2 - 10.1109/ICCA51439.2020.9264331
DO - 10.1109/ICCA51439.2020.9264331
M3 - Conference contribution
AN - SCOPUS:85098053321
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 1494
EP - 1499
BT - 2020 IEEE 16th International Conference on Control and Automation, ICCA 2020
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Control and Automation, ICCA 2020
Y2 - 9 October 2020 through 11 October 2020
ER -