Abstract
In the present paper, we discussed the implementation of a novel online optimal neuron-controller which based on improved Action-Depended Dual Heuristic Dynamic Programming (ADDHP) algorithm, including its schematic diagram, and the training algorithms. This algorithm requires neither an explicit model of controlled system nor the indispensable system performance index ‘J’. Compared with standard ADDHP, the proposed method only use the states of present and previous time step to calculate the derivative of the performance function, avoiding predicting the states of next time step, so the model network can be omitted completely. It makes the configuration of the method become simple, more suitable for complex nonlinear systems. The simulation control example is conducted in this paper, and the results show that the proposed ADDHP-based optimal neuron-controller has advantages in fast response, anti-jamming capability and robustness.
The work was supported in part by: National Science Foundation of Guangxi Province of China under Grant 0991057; Guangxi natural sciences fund (GSF 0575016); the Science & Research Foundation of Educational Commission of Guangxi Province of China under Grant 2008[27].
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References
Werbos, P.J.: Advanced forecasting methods for global crisis warning and models of intelligence. J. General System Yearbook 22, 25–38 (1977)
Prokhorov, D.V.: Adaptive Critic Designs and their Applications. Thesis. Texas Tech University, TX (1997)
Bertsekas, D.P.: Dynamic Programming and Optimal Control. J. Athena Scientific, Belmont, MA (1995)
Bellman, R.E.: Dynamic programming. Princeton University Press, NJ (1957)
Danil, P., Don, W.: Adaptive Critic Designs. IEEE Trans. on Neural Networks 8, 997–1007 (1997)
Jennie, S., Wang, Y.T.: On-Line Learning Control by Association and Reinforcement. IEEE Trans. on Neural Networks 12, 264–276 (2001)
John, J.M., Chadwick, J.C., George, G.L., Richard, S.: Adaptive Dynamic Programming. IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews 32, 140–153 (2002)
Liu, D.R., Xiong, X.X., Zhang, Y.: Action-Dependent Adaptive Critic Designs. IEEE Neural Networks Proceedings, 990–995 (2001)
Park, J.W., Harley, R.G., Venayagamoorthy, G.K.: Dual heuristic programming based nonlinear optimal control for a synchronous generator. Engineering Applications of Artificial Intelligence 21, 97–105 (2008)
Zhang, H., Wei, Q., Liu, D.: On-line learning control for discrete nonlinear systems via an improved ADDHP method. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4491, pp. 387–396. Springer, Heidelberg (2007)
Shiah, C.: Abstract of the Dissertation Natural Adaptive Critic Design for Control and its Parallel Implementation. State University of New York, New York (2002)
Song, S., Li, J., Lin, X.: Action-dependent heuristic dynamic programming for level control of three tanks. In: Proc. Chin. Control. Conf., Kunming, China, CCC, July 16-18, pp. 468–4739 (2008)
Zhou, D.: Introduction of Adaptive Control on Non-linear Systems, pp. 53–55. Tsinghua University Press, Beijing (2002)
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Song, S., Cai, G., Lin, X. (2009). Optimal Neuron-Controller for Fluid Triple-Tank System via Improved ADDHP Algorithm. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_49
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DOI: https://doi.org/10.1007/978-3-642-03156-4_49
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