ICONIP 2008: Advances in Neuro-Information Processing pp 1053-1059 | Cite as
Economical Implementation of Control Loops for Multi-robot Systems
Abstract
In spite of the multiple advantages that multi-robot systems offer, to turn them into a realistic option and to get their proliferation, they must be economically attractive. Multi-robot systems are composed of several robots that generally are similar, so if an economic optimization is done in one of them, such optimization can be replicated in each member. In this paper we deal with the economic optimization of each control loops of the subsystems that each robot must control individually. As the subsystems can be complex, we propose to use a Predictive Control modeled by Time Delayed Neural Networks and implemented using very low cost Field Programmable Gate Arrays.
Keywords
Control Loop Model Predictive Control Predictive Controller Economic Optimization FPGA DevicePreview
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References
- 1.Stancliff, S.B., Dolan, J.M., Trebi-Ollennu, A.: Mission Reliability Estimation for Multirobot Team Design. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2206–2211 (2006)Google Scholar
- 2.O’Hara, K.J., Balch, T.R.: Pervasive Sensor-less networks for cooperative multi-robot tasks. In: 7th International Symposium on Distributed Autonomous Robotic Systems (DARS 2004), pp. 305–314 (2007)Google Scholar
- 3.Wu, H., Tian, G., Huang, B.: Multi-robot collaborative localization methods based on Wireless Sensor Network. In: IEEE International Conference on Automation and Logistics, pp. 2053–2058 (2008)Google Scholar
- 4.Kornienko, S., Kornienko, O., Levi, P.: Minimalistic approach towards communication and perception in microrobotic swarms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2228–2234 (2005)Google Scholar
- 5.Andrews, B.W., Passino, K.M., Waite, T.A.: Social Foraging Theory for Robust Multiagent System Desing. IEEE Transactions on Automation Science and Engineering 4(1), 79–86 (2007)CrossRefGoogle Scholar
- 6.Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2004)MATHGoogle Scholar
- 7.Camacho, E.F., Bordons, C.: Model Predictive Control in the Process Industry. Springer, London (1995)CrossRefGoogle Scholar
- 8.Maciejowski, J.M.: Predictive Control with Constraints. Prentice Hall, London (2002)MATHGoogle Scholar
- 9.Sunan, H., Kok, T., Tong, L.: Applied Predictive Control. Springer, London (2002)CrossRefGoogle Scholar
- 10.Braspenning, P.J., Thuijsman, F., Weijters, A.J.M.M.: Artificial Neural Networks. Springer, Berlin (1995)CrossRefMATHGoogle Scholar
- 11.Chester, M.: Neural Networks. Prentice Hall, New Jersey (1993)MATHGoogle Scholar
- 12.Widrow, B., Lehr, M.A.: 30 Years of Adaptative Neural Networks: Perceptron, Madaline, and Backpropagation. Proceedings of IEEE 78(9), 1415–1441 (1990)CrossRefGoogle Scholar
- 13.Huang, B.Q., Rashid, T., Kechadi, M.T.: Multi-Context Recurrent Neural Network for Time Series Applications. International Journal of Computational Intelligence 3(1), 45–54 (2006)Google Scholar
- 14.Narendra, K.S., Parthasarathy, K.: Indentification and Control of Dynamical Systems Using Neural Networks. IEEE Tran. Neural Networks 1(1), 491–513 (1990)CrossRefGoogle Scholar
- 15.Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2003)MATHGoogle Scholar
- 16.Arahal, M.R., Berenguel, M., Camacho, E.F.: Neural identification applied to predictive control of a solar plant. Control Engineering Practice 6, 333–344 (1998)CrossRefGoogle Scholar
- 17.Huang, J.Q., Lewis, F.L., Liu, K.: A Neural Net Predictive Control for Telerobots with Time Delay. Journal of Intelligent and Robotic Systems 29, 1–25 (2000)CrossRefMATHGoogle Scholar