Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks
In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.
KeywordsProbability Density Function Dynamic Neural Network Tracking Control Problem Compensation Term Conditional Probability Density Function
Unable to display preview. Download preview PDF.
- 2.Forbes, M.J., Forbes, J.F., Guay, M.: Regulatory Control Design for Stochastic Processes: Shaping the Probability Density Function. In: Proc. ACC, Denver, USA, pp. 3998–4003 (2003)Google Scholar
- 6.Brown, M., Harris, C.J.: Neurofuzzy Adaptive Modeling and Control. Prentice-Hall, Englewood Cliffs (1994)Google Scholar
- 7.Poznyak, A.S., Yu, W., Sanchez, E.N., Perez, J.P.: Nonlinear Adaptive Trajectory Tracking Using Dynamic Neural Networks. IEEE Trans. Neural Networks 6, 402–1411 (1999)Google Scholar
- 8.Yu, W., Li, X.O.: Some New Results on System Identification with Dynamic Neural Networks. IEEE Trans. Neural Networks 12, 412–417 (2002)Google Scholar