Learning control of mobile robots using a multiprocessor system

Control Engineering Practice

Volumn 14, Issue 11, 2006, Pages 1279-1295

  Antonini, P ^a   Ippoliti, G ^a   Longhi, S ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

Autonomous vehicles; Multiple models; Multiprocessor systems; Neural networks; Robotics

Indexed keywords

AUTONOMOUS VEHICLES; MULTIPLE MODELS; MULTIPROCESSOR SYSTEM;

APPROXIMATION THEORY; IDENTIFICATION (CONTROL SYSTEMS); MULTIPROCESSING SYSTEMS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REAL TIME SYSTEMS; ROBOTICS; TIME VARYING CONTROL SYSTEMS;

MOBILE ROBOTS;

APPROXIMATION THEORY; IDENTIFICATION (CONTROL SYSTEMS); MOBILE ROBOTS; MULTIPROCESSING SYSTEMS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REAL TIME SYSTEMS; ROBOTICS; TIME VARYING CONTROL SYSTEMS;

EID: 33745216921     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2005.06.012     Document Type: Article

References (32)

1. Ahmed-Zaid, F., Ioannou, P. A., Polycarpou, M. M., & Youssef, H. M. (1993). Identification and control of aircraft dynamics using radial basis function networks. In Proceedings of the second IEEE conference on control applications (pp. 567-572). Vancouver BC.
2. Vancouver, BC. Angeloni, A., Leo, T., Longhi, S., Zulli, R. (1996). Real time collision avoidance for mobile robots. In Proceedings of the sixth symposium on measurement and control in robotics (ISMCR 96) (pp. 239-244). Brussels Belgium.
3. Brussels, Belgium. Chen, S., Cowan, C. F. N., & Grant, P. M. (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2(2), 302-309.
4. Chen, L., & Narendra, K. S. (2000). Nonlinear adaptive control using neural networks and multiple models. In Proceedings of the 2000 American control conference (vol. 6, pp. 4199-4203).
5. Conte, G., Longhi, S., & Zulli, R. (1995). Nonholonomic motion planning using distance field. In Proceedings of the 1995 IFAC conference on intelligent autonomous vehicles (pp. 93-98). Helsinky Finland.
6. Conte, G., Longhi, S., & Zulli, R. (1996). Motion planning for unicycle and car-like robots. International Journal of Systems Science, 27(8), 791-798.
7. Corradini, M., Ippoliti, G., & Longhi, S. (2003). Neural networks based control of mobile robots: Development and experimental validation. Journal of Robotic Systems, 20(10), 587-600.
8. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signal and Systems, 2(4), 303-314.
9. D'Amico, A., Ippoliti, G., Longhi, S. (2001a). Adaptation and learning in neural networks multiple models based control of mobile robots. In Proceedings of the 2001 IFAC workshop on adaptation and learning in control and signal processing (ALCOSP 2001) (pp. 199-206). Cernobbio-Como Italy.
10. D'Amico, A., Ippoliti, G., Longhi, S. (2001b). A radial basis function networks approach for the tracking problem of mobile robots. In Proceedings of the 2001 IEEE/ASME international conference on advanced intelligent mechatronics (AIM '01) (pp. 498-503). Como Italy.
11. de Wit, C. C., & Khennouf, H. (1995). Quasi-continuous stabilizing controllers for nonholonomic systems: Design and robustness considerations. In Proceedings of the third European control conference (pp. 2630-2635). Rome, Italy.
12. de Wit, C. C., Khennouf, H., Samson, C., & Sordalen, O. J. (1993). Nonlinear control design for mobile robots. In Y. F. Zheng (Ed.), Recent trends in mobile robots (pp. 121-156). 1 Singapore: World Scientific.
13. Girosi, F., & Poggio, T. (1990). Neural networks and the best approximation property. Biological Cybernetics, 63, 169-176.
14. Hunt, K. J., Sbarbaro, D., Zbikowski, R., & Gawthrop, P. J. (1992). Neural networks for control systems - a survey. Automatica, 28(6), 1083-1112.
15. Hush, D. R., & Horne, B. G. (1993). Progress in supervised neural networks. IEEE Signal Processing Magazine, 10, 8-39.
16. Ippoliti, G., & Longhi, S. (2004). Multiple models for adaptive control to improve the performance of minimum variance regulators. In IEE Proceedings - Control Theory and Applications (vol. 151, no. 2, pp. 210-217). Oxford UK.
17. Jagannathan, S., Lewis, F., Pastravanu, O. (1994). Model reference adaptive control of nonlinear dynamical systems using multilayer neural networks. In Proceedings of the 1994 IEEE World congress on computational intelligence, 1994 IEEE international conference on neural networks (pp. 4766-4771). Orlando FL, USA.
18. Jetto, L., Longhi, S., & Venturini, G. (1999). Development and experimental validation of an adaptive extended kalman filter for the localization of mobile robots. IEEE Transactions on Robotics & Automation, 15, 219-229.
19. Jetto, L., Longhi, S., & Vitali, D. (1999). Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted kalman filter. Control Engineering Practice, 7, 763-771.
20. Mihram, G. A. (1972). Simulation: Statistical foundations and methodology. Mathematics in science and engineering (vol. 92). New York, USA: Academic Press.
21. Morse, A. S. (1995). Control using logic-based switching. In A. Isidori (Ed.), Trends in control: A European perspective (pp. 69-113). London Great Britain: Springer.
22. Narendra, K. S., & Xiang, C. (2000). Adaptive control of discrete-time systems using multiple models. IEEE Transactions on Automatic Control, 45(9), 1669-1686.
23. Polycarpou, M. M. (1996). Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, 41(3), 447-451.
24. Polycarpou, M. M., & Ioannou, P. A. (1995). Adaptive bounding techniques for stable neural control systems. In Proceedings of the 34th conference on decision and control (pp. 2442-2447). New Orleans, LA.
25. Sadegh, N. (1995). A nodal link perceptron network with applications to control of a nonholonomic system. IEEE Transactions on Neural Networks, 6(6), 1516-1523.
26. Sanner, R. M., & Essex, C. (1996). Multiresolution radial basis function networks for the adaptive control of robotic systems. In UKACC international conference on control '96 (pp. 894-898).
27. Sanner, R. M., & Slotine, J.-J. E. (1991). Stable adaptive control and recursive identification using radial gaussian networks. In Proceedings of the 30th conference on decision and control (pp. 2116-2123). Brighton England.
28. Sanner, R. M., & Slotine, J.-J. E. (1994). Function approximation, "neural" networks, and adaptive nonlinear control. In Proceedings of the third IEEE conference on control applications (pp. 1225-1232). Glasgow UK.
29. Unar, M. N., & Murray-Smith, D. J. (1999). Automatic steering of ships using neural network. International Journal of Adaptive Control and Signal Processing, 13, 203-218.
30. Warwick, K., Kambhampati, C., Parks, P., & Mason, J. (1996a). Dynamic systems in neural networks. In K. Hunt, G. Irwin, & K. Warwick (Eds.), Neural network engineering in dynamic control systems (pp. 27-41). London Great Britain: Springer.
31. Warwick, K., Kambhampati, C., Parks, P., & Mason, J. (1996b). Dynamic systems in neural networks. In K. Hunt, G. Irwin, & K. Warwick (Eds.), Neural network engineering in dynamic control systems (pp. 27-41). London, Great Britain: Springer.
32. Weller, S. R., & Goodwin, G. C. (1994). Hysteresis switching adaptive control of linear multivariable systems. IEEE Transactions on Automatic Control, 39(7), 1360-1375.