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2015 | OriginalPaper | Chapter

Real-Time Robust Model Predictive Control of Mobile Robots Based on Recurrent Neural Networks

Authors : Shuzhan Bi, Guangfei Zhang, Xijun Xue, Zheng Yan

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

This paper presents a novel model predictive control (MPC) approach to tracking control of mobile robots based on recurrent neural networks (RNNs). The tracking control problem is firstly formulated as a sequential dynamic optimization problem in framework of MPC. Then a novel neurodynamic approach is developed for computing the optimal control signals in real time, where multiple RNNs are applied in a collective fashion. The proposed approach enables MPC of mobile robots to be synthesized in real time. Simulation results are provided to substantiate the effectiveness of the proposed approach.

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previous chapter Robust Parameter Estimation of Gaussian Mixture Models: Comparison with Expectation Maximization
next chapter Dynamical Analysis of Neural Networks with Time-Varying Delays Using the LMI Approach
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Metadata
Title
Real-Time Robust Model Predictive Control of Mobile Robots Based on Recurrent Neural Networks
Authors
Shuzhan Bi
Guangfei Zhang
Xijun Xue
Zheng Yan
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26554-4

Electronic ISBN: 978-3-319-26555-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26555-1_33

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