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Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network
Tech Univ Munich, Dept Informat, D-85748 Munich, Germany..
Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy..
KTH, School of Engineering Sciences (SCI), Centres, BioMEx. KTH, School of Engineering Sciences (SCI), Mechanics.
Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen 518052, Peoples R China..
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2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 3, article id 64Article in journal (Refereed) Published
Abstract [en]

The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 8, no 3, article id 64
Keywords [en]
mobile robot, nonlinear model predictive control, quadratic programming, varying-parameter convergent differential neural network
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262804DOI: 10.3390/robotics8030064ISI: 000487977200003Scopus ID: 2-s2.0-85072753538OAI: oai:DiVA.org:kth-262804DiVA, id: diva2:1362520
Note

QC 20191021

Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21Bibliographically approved

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Zhang, Longbin

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