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Adaptive neural design frame for uncertain stochastic nonlinear non-lower triangular pure-feedback systems with input constraint. (English) Zbl 1423.93171

Summary: This paper dedicates to dealing with the adaptive neural design problem for uncertain stochastic nonlinear systems with non-lower triangular pure-feedback form and input constraint. On the basis of the mean-value theorem, the pure-feedback structure is first transformed into the desired affine structure, and then the well-known backstepping technology is applied to construct the actual input signal of the controller. Although the considered system has a fairly complex structure, a new adaptive neural tracking controller design frame is established via the flexible application of radial basis function (RBF) neural networks’ (NNs’) structural characteristics. The proposed design frame guarantees the control objective of this paper can be achieved. Finally, a simulation example is given to further illustrate the availability of the proposed control scheme.

MSC:

93C40 Adaptive control/observation systems
93B52 Feedback control
93C41 Control/observation systems with incomplete information
93E03 Stochastic systems in control theory (general)
93C10 Nonlinear systems in control theory
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