Optimizing central pattern generators (CPG) controller for one legged hopping robot by using genetic algorithm (GA)

  • Abstract
  • Keywords
  • References
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  • Abstract

    This paper presents the optimization process of Central Pattern Generator (CPG) controller for one legged hopping robot by using Genetic Algorithm (GA). To control the one legged hopping robot, a CPG controller is designed and integrated with a conventional Proportional-Integral (PI) controller. Conventionally, the CPG parameters are tuned manually. But by using this method, the parameters produced are not exactly the optimum parameters for the CPG. Therefore, a computational stochastic optimization method; GA is designed to optimize the CPG controller parameters. The GA is designed based on minimizing the error produced towards achieving the reference height. The re-sponse of the one legged hopping robot is compared and the results of the error towards reference height are analyzed.



  • Keywords

    One Legged; Hopping; CPG; PI; Genetic Algorithm

  • References

      [1] Bauer C, Braun S, Chen Y, Jakob W & Mikut R (2006), Optimization of artificial central pattern generators with evolutionary algorithms. Proceedings of the 18th Workshop Computational Intelligence, pp. 40–54.

      [2] Larsen JC, Central pattern generators in modern science.

      [3] Matsuoka K (1985), Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics 52, 367–376.

      [4] Matsuoka K (1987), Mechanisms of frequency and pattern control in the neural rhythm generators. Biological Cybernetics 56, 345–353.

      [5] Taga G, Yamaguchi Y & Shimizu H (1991), Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics 65, 147–159.

      [6] Taga G (1992), Modeling and simulation of biped locomotion. Journal of Society of Biomechanism of Japan 16, 209–214.

      [7] Inada H & Ishii K (2004), Bipedal walk using a central pattern generator. International Congress Series 1269, 185–188.

      [8] Arena P (2001), a mechatronic lamprey controlled by analog circuits. Proceedings of the ninth IEEE Mediterranean Conference on Control and Automation.

      [9] Kassim AB & Yasuno T (2010), Moving control of quadruped hopping robot using adaptive CPG networks. Proceedings of the IEEE Conference on Robotics Automation and Mechatronics, pp. 581–588.

      [10] Rahim NH, Kassim AM, Miskon MF & Azahar AH (2011), Effectiveness of central pattern generator model on developed one-legged hopping robot. Proceedings of the IEEE Student Conference on Research and Development, pp. 85–88.

      [11] Azahar AH, Horng CS & Kassim AM (2013), Vertical motion control of a one legged hopping robot by using central pattern generator (CPG). Proceedings of the IEEE Symposium on Industrial Electronics and Applications, pp. 7–12.

      [12] Hooper SL (2001), Central pattern generator. https://pdfs.semanticscholar.org/521c/c0324b14160bbdb9e77c16877bda734a21ef.pdf.

      [13] Malhotra R, Singh N & Singh Y (2011), Genetic algorithms: Concepts, design for optimization of process controllers. Computer and Information Science 4, 39–54.

      [14] Karthikraja A, Petchinathan G & Ramesh S (2009), stochastic algorithm for PID tuning of bus suspension system. Proceedings of the IEEE International Conference on Control, Automation, Communication and Energy Conservation, pp. 1–6.

      [15] Vladu EE & Dragomir TL (2004), Controller tuning using genetic algorithms. Proceedings of the first Romanian-Hungarian Joint Symposium on Applied Computational Intelligence, pp. 1–10.

      [16] Chen Y, Bauer C, Burmeister O, Rupp R & Mikut R (2007), First steps to future applications of spinal neural circuit models in neuroprostheses and humanoid robots. Proceedings of the 17th Workshop Computational Intelligence, pp. 186–199.




Article ID: 12817
DOI: 10.14419/ijet.v7i2.14.12817

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