期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 46, 期 6, 页码 740-749出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2015.2465352
关键词
Model predictive controls (MPCs); neural-dynamic optimization; quadratic programming (QP) problem; trajectory tracking
资金
- Natural Science Foundation of China [61174045, 61573147]
- China National Funds for Distinguished Young Scientists [61425009]
- Program for New Century Excellent Talents in University [NCET-12-0195]
- Guangzhou Research Collaborative Innovation Projects [2014Y2-00507]
- National High-Tech Research and Development Program of China (863 Program) [2015AA042303]
Mobile robots tracking a reference trajectory are constrained by the motion limits of their actuators, which impose the requirement for high autonomy driving capabilities in robots. This paper presents a model predictive control (MPC) scheme incorporating neural-dynamic optimization to achieve trajectory tracking of nonholonomic mobile robots (NMRs). By using the derived tracking-error kinematics of nonholonomic robots, the proposed MPC approach is iteratively transformed as a constrained quadratic programming (QP) problem, and then a primal-dual neural network is used to solve this QP problem over a finite receding horizon. The applied neural-dynamic optimization can make the cost function of MPC converge to the exact optimal values of the formulated constrained QP. Compared with the existing fast MPC, which requires repeatedly calculating the Hessian matrix of the Langragian and then solves a quadratic program. The computation complexity reaches O(n(3)), while the proposed neural-dynamic optimization contains O(n(2)) operations. Finally, extensive experiments are provided to illustrate that the MPC scheme has an effective performance on a real mobile robot system.
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