Gain and Order Scheduling for Fractional Order Controllers
In many real world scenarios, the model of the controlled process changes over time. Clearly in such situations the parameters of the controller need to be adjusted to give an acceptable level of control system performance. Many adaptive control techniques exist to cope with such changes in system dynamics. Traditionally since PID controllers have been extensively used in industrial processes, the adaptation laws looked at changing the proportional, integral and derivative gains of the controllers. With the use of Fractional order PID controllers, both the gains and the differentiation and integration orders may be fine-tuned online, to achieve a better system performance. This chapter looks at integration of computational intelligence paradigms with fractional order adaptive control and the various advantages that this synergism can offer.
KeywordsFractional Order Network Control System Genetic Algorithm Packet Dropout Gain Schedule
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- Almutairi, N.B., Chow, M.Y., Tipsuwan, Y.: Network-based controlled DC motor with fuzzy compensation. In: The 27th Annual Conference of the IEEE Industrial Electronics Society, IECON 2001, vol. 3, pp. 1844–1849 (2001)Google Scholar
- Chow, M.Y., Tipsuwan, Y.: Network-based control systems: a tutorial. In: The 27th Annual Conference of the IEEE Industrial Electronics Society, IECON 2001, vol. 3, pp. 1593–1602 (2001)Google Scholar
- Das, S.: Functional fractional calculus. Springer (2011)Google Scholar
- Das, S., Saha, S., Mukherjee, A., et al.: Adaptive Gain and Order Scheduling of Optimal Fractional Order PIλDμ Controllers with Radial Basis Function Neural-Network. In: 2011 International Conference on Process Automation, Control and Computing (PACC), pp. 1–6 (2011)Google Scholar
- Eriksson, L.M., Johansson, M.: Simple PID tuning rules for varying time-delay systems. In: 2007 46th IEEE Conference on Decision and Control, pp. 1801–1807 (2007)Google Scholar
- Fan, X., Meng, F., Fu, C., et al.: Research of brushless dc motor simulation system based on RBF-PID algorithm. In: 2009 KAM 2009 Second International Symposium on Knowledge Acquisition and Modeling, vol. 2, pp. 277–280 (2009)Google Scholar
- Fliess, M., Join, C.: Intelligent PID controllers. In: 2008 16th Mediterranean Conference on Control and Automation, pp. 326–331 (2008)Google Scholar
- Sadati, N., Ghaffarkhah, A., Ostadabbas, S.: A new neural network based FOPID controller. In: IEEE International Conference on Networking, Sensing and Control, ICNSC 2008, pp. 762–767 (2008)Google Scholar
- Sano, Y., Kita, H.: Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 360–365 (2002)Google Scholar
- Zhang, M., Li, W., Liu, M.: Adaptive PID control strategy based on RBF neural network identification. In: International Conference on Neural Networks and Brain, ICNN&B 2005, vol. 3, pp. 1854–1857 (2005)Google Scholar
- Zhang, M.G., Li, W.H.: Single neuron PID model reference adaptive control based on RBF neural network. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 3021–3025 (2006)Google Scholar
- Zhao, J., Li, P., Wang, X.S.: Intelligent PID controller design with adaptive criterion adjustment via least squares support vector machine. In: Control and Decision Conference, CCDC 2009, Chinese, pp. 7–12 (2009)Google Scholar