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Open AccessArticle

Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network

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Department of Informatics, Technical University of Munich, 85748 Munich, Germany
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Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
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BioMEx Center & KTH Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518052, China
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College of Automotive Engineering, Tongji University, Shanghai 201804, China
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Author to whom correspondence should be addressed.
Robotics 2019, 8(3), 64; https://doi.org/10.3390/robotics8030064
Received: 29 June 2019 / Revised: 21 July 2019 / Accepted: 26 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Robotics and Automation Engineering)
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. View Full-Text
Keywords: mobile robot; nonlinear model predictive control; quadratic programming; varying-parameter convergent differential neural network mobile robot; nonlinear model predictive control; quadratic programming; varying-parameter convergent differential neural network
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MDPI and ACS Style

Hu, Y.; Su, H.; Zhang, L.; Miao, S.; Chen, G.; Knoll, A. Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network. Robotics 2019, 8, 64.

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