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An ISS-modular approach for adaptive neural control of pure-feedback systems |
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| Authors: | Cong Wang [Author Vitae] David J Hill [Author Vitae] |
| Affiliation: | a College of Automation, South China University of Technology, Guangzhou 510641, China b Research School of Information Sciences and Engineering, The Australian National University, Australia c Department of Electrical and Computer Engineering, The National University of Singapore, Singapore d Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China |
| Abstract: | Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called “ISS-modularity” of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach. |
| Keywords: | Adaptive neural control Pure-feedback systems Non-affine systems Input-to-state stability Small-gain theorem |
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