Control of Nonlinear Stochastic Systems

  • Hong Wang
Part of the Advances in Industrial Control book series (AIC)


The work presented so far has been focussed on linear dynamic systems where several modelling and control algorithms have been developed and are shown to work well for some linear systems ([41, 44]). However, due to the wide existence of nonlinear systems in practice, it is also important to investigate the control algorithms for the control of the output probability density functions for general nonlinear stochastic systems. This forms the main purpose of this chapter, where an extended solution to the control of nonlinear systems will be described. We will present two solutions, one for a specific class of nonlinear system and the other for a general system as given in Equation (2.8.1). The former will involve the use of a one-step-ahead nonlinear predictor whilst the later will need the use of Multi-Layer Perceptron (MLP) neural networks ([49]). The stability of the closed loop control for the specific class of nonlinear system will also be formulated.


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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Hong Wang
    • 1
  1. 1.Department of Paper ScienceUMISTManchesterUK

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