Abstract
This paper studies the problem of controlling the movements of a handicapped person's motorized wheelchair from a practical point of view. The control system implemented has been divided into two levels: the “low level”, consisting of an electronic system which directly controls the drivers of the chair's motors, with a classic PID (proportional-integral-derivative) control loop. The aim of this level is to ensure that the speeds of each one of the wheels is similar to the input speed of these control boards. The second control level (“high level”), implemented by means of neural techniques, ensures that the linear and angular speeds of the wheelchair are those indicated by a trajectory generator. A new recurrent model is used as the neural network, for which the stability conditions of the complete control system are obtained and various practical tests are carried out, which show the correct performance of the actual system implemented.
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References
Boquete, L. et al.: Identification of systems using radial basis networks feedbacked with FIR filters, in: Internat. Conf. on Computational Intelligence. Theory and Applications, Dortmund, 1997, Lectures Notes in Computer Science, pp. 46–51.
García, R. et al.: Aplicación de la tecnología lonworks para el control de bajo nivel de un robot móvil, in: Seminario Anual de Automática y Electrónica Industrial SAEEI'97, Valencia, Spain, Septiembre 1997, pp. 132–138.
Hunt, K. J. and Sbarbaro, D.: Neural networks for nonlinear internal model control, IEE Proceedings d 138(5) (1991).
Ku, C. C. and Lee, K. Y.: Diagonal recurrent neural networks for dynamic systems control, IEEE Trans. Neural Networks 6(1) (1995).
Maeda, Y. and Figueiredo, R. J. P.: Learning rules for neuro-controller via simultaneous perturbation, IEEE Trans. Neural Networks 8(5) (1997).
Narendra, K. and Parthasarathy: Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks 1(1) (1990), 4–26.
Narendra, K.: Neural networks for control: Theory and practice, Proc. IEEE 84(10) (1996).
Noriega, J. R. and Wang, H.: A direct adaptive neural-network control for unknown nonlinear systems and its application, IEEE Trans. Neural Networks 9(1) (1998).
Venugopal, K.: Learning in connectionist networks using the alopex algorithm, PhD Thesis, Florida Atlantic University, Boca Raton, FL, April 1993.
Zhang, Y., Pratyush, S. and Grant, E. H: An on-line trained adaptive neural controller, IEEE Control Systems (October 1995).
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Boquete, L., García, R., Barea, R. et al. Neural Control of the Movements of a Wheelchair. Journal of Intelligent and Robotic Systems 25, 213–226 (1999). https://doi.org/10.1023/A:1008068322312
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DOI: https://doi.org/10.1023/A:1008068322312