Adaptive Intelligent Systems applied to two-wheeled robot and the effect of different terrains on performance

Adaptive Intelligent Systems applied to two-wheeled robot and the effect of different terrains on performance

Volume 2, Issue 1, Page No 1-5, 2017

Author’s Name: Sender Rocha dos Santosa),1, Jorge L. M. Amaral, José Franco M. Amaral

View Affiliations

Department of Electronics and Telecommunication Engineering, Rio de Janeiro State University, UERJ, Rio de Janeiro, 20550-900, Brazil

a)Author to whom correspondence should be addressed. E-mail: senderrocha@yahoo.com.br

Adv. Sci. Technol. Eng. Syst. J. 2(1), 1-5 (2017); a DOI: 10.25046/aj020101

Keywords: Two wheeled robot, Neuro-fuzzy control, Artificial neural net

Share

381 Downloads

Problem Downloading File? Alternate Link

Export Citations

This work discuss two different intelligent controllers: Online Neuro Fuzzy Controller (ONFC) and Proportional-Integral-Derivative Neural Network (PID-NN). They were applied to maintain the equilibrium and to control the position of a two-wheeled robot prototype. Experiments were carried out to investigate the equilibrium control and movement of the two-wheeled robot first on flat terrain, then in other situations, where terrain may not be flat, horizontal surface. The effectiveness of each controller was verified by experimental results, and the performance was compared with conventional PID control scheme applied for the prototype.

Received: 26 November 2016, Accepted: 22 December 2016, Published Online: 28 January 2017

  1. Deegan, B. Thibodeau, and R. Grupen, “Designing a self-stabilizing robot for dynamic mobile manipulation”, In Proceedings of the Robotics: Science and Systems Workshop on Manipulation for Human Environments, Philadelphia, Pennsylvania (2006).
  2. H. Lee, and S. Jung, “Line Tracking Control of a Two-Wheeled Mobile Robot Using Visual Feedback”, International Journal of Advanced Robotic Systems (2013).
  3. Su and Y. Chen, “Balance Control for Two-Wheeled Robot via Neural-Fuzzy Technique”, SICE Annual Conference (2010).
  4. P. M. Chan, K. A. Stol, C. R. Halkyard “Review of modeling and control of two-wheeled robots”, Annual reviews in control 37: 89-103 (2013).
  5. Jin, “Development of a Stable Control System for a Segway,” Royal Institute of Technology (2013).
  6. R. Gouvêa, “Controle Neurofuzzy de Motor de Indução Com Estimação de Parâmetros e Fluxo de Estator”, Tese de Doutorado, Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, MG (2005).
  7. V. Pires, “Controladores baseados em técnicas de inteligência computacional: Análise, projeto e aplicação,” Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, MG (2007).
  8. M. Spadini, P. E. Silva, L. F. F. Campos, C. J. F. Araújo, and A, Nied, “Desenvolvimento de Controle Neurofuzzy para Plantas Não Lineares: Aplicação em Tanques Acoplados”, Simpósio Brasileiro Automação Inteligente, SBAI (2013).
  9. C. K. Ferrari, “Controlador PID sintonizado por redes neurais artificiais”, Monografia, Curso de Engenharia Elétrica, Universidade Federal do Paraná, Curitiba (2011).
  10. R. Bageant, “Balancing a Two-Wheeled Segway Robot”, Thesis (S.B.)-Massachusetts Institute of Technology, Dept. of Mechanical Engineering, (2011).
  11. Costa, “A practical implementation of self-evolving cloud-based control of a pilot plant”, in Proceedings of 2013 IEEE International Conference on Cybernetics (CYBCONF 2013), Lausanne, Switzerland, 7-12 (2013).
  12. Precup, M. Radac, M. L. Tomescu, E. M. Petriu, S. Preiti, “Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems”, Expert Systems with Applications, 40(1): 188-199 (2013).
  13. Castro, R. Carballo, G. Iglesias, J.R. Rabunal, “Performance of artificial neural networks in nearshore wave power prediction”, Applied Soft Computing, 23: 194-201 (2014).