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• W2801497431 abstract "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-bearing-orientation 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." @default.
• W2801497431 created "2018-05-17" @default.
• W2801497431 creator A5014474514 @default.
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• W2801497431 date "2018-12-01" @default.
• W2801497431 modified "2023-10-12" @default.
• W2801497431 title "Neural-Dynamic Optimization-Based Model Predictive Control for Tracking and Formation of Nonholonomic Multirobot Systems" @default.
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• W2801497431 doi "https://doi.org/10.1109/tnnls.2018.2818127" @default.
• W2801497431 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29993700" @default.
• W2801497431 hasPublicationYear "2018" @default.
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