Adaptive neural design frame for uncertain stochastic nonlinear non-lower triangular pure-feedback systems with input constraint |
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| Authors: | Rui-Bing Li Ben Niu Zhi-Guang Feng Jun-Qing Li Pei-Yong Duan Dong Yang |
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| Institution: | 1. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;2. School of Automation, Harbin Engineering University, Harbin 150001, China;3. School of Computer, Liaocheng University, Liaocheng 252059, China;4. School of Engineering, Qufu Normal University, Rizhao 276826, China |
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| Abstract: | 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. |
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| Keywords: | Corresponding author |
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