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An Evolutionary Radial Basis Function Neural Network with Robust Genetic-Based Immunecomputing for Online Tracking Control of Autonomous Robots

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Abstract

This paper presents an evolutionary radial basis function neural network with genetic algorithm and artificial immune system (GAAIS-RBFNN) for tracking control of autonomous robots. Both the GAAIS-RBFNN computational intelligence and online tracking controller are implemented in one field-programmable gate array (FPGA) chip to cope with the optimal control problem of real-world mobile robotics. The hybrid GAAIS paradigm incorporated with Taguchi quality method is employed to determine the optimal structure of RBFNN. The control parameters of tracking controller are online tuned by minimizing the performance index using the proposed GAAIS-RBFNN to achieve trajectory tracking. Experimental results and comparative works are conducted to show the effectiveness and merit of the proposed FPGA-based GAAIS-RBFNN tracking controller using system-on-a-programmable-chip technology. This FPGA-based online hybrid GAAIS-RBFNN intelligent controller outperforms the existing bio-inspired RBFNN controllers using individual GA and AIS algorithms.

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Acknowledgments

The authors gratefully acknowledge financial support from the Ministry of Science and Technology, Taiwan, R.O.C., under Grant MOST 103-2221-E-197-028.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Ilan University, Yilan, Taiwan

    Hsu-Chih Huang & Chih-Hao Chiang

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  1. Hsu-Chih Huang
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  2. Chih-Hao Chiang
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Correspondence to Hsu-Chih Huang.

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Huang, HC., Chiang, CH. An Evolutionary Radial Basis Function Neural Network with Robust Genetic-Based Immunecomputing for Online Tracking Control of Autonomous Robots. Neural Process Lett 44, 19–35 (2016). https://doi.org/10.1007/s11063-015-9452-3

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