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

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• 作者：Hsu-Chih Huang ; Chih-Hao Chiang
• 刊名：Neural Processing Letters
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：44
• 期：1
• 页码：19-35
• 全文大小：1,063 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X
• 卷排序：44

文摘

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.KeywordsAISMobile robotRBFNNTracking control