Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 12, Pages 6113-6122Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2818127
Keywords
Formation control; multiple mobile robots; neural-dynamic optimization; nonlinear model predictive control (NMPC)
Categories
Funding
- National Natural Science Foundation of China [61573147, 61625303, 61751310]
- Guangdong Science and Technology Research Collaborative Innovation Projects [2014B090901056, 2015B020214003, 2016A020220003]
- Guangdong Science and Technology Plan Project (Application Technology Research Foundation) [2015B020233006]
- Anhui Science and Technology Major Program [17030901029]
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In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearingorientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader-follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader-follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.
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