Adaptive neural control for uncertain stochastic nonlinear strict-feedback systems with time-varying delays: A Razumikhin functional method |
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| Authors: | Zhaoxu Yu Hongbin Du |
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| Affiliation: | 1. Computational Science and Engineering, Qatar Computing Research Institute, Doha, Qatar;2. Department of Intelligent Systems, Delft University of Technology, The Netherlands;1. Department of Applied Mathematics, Xidian University, No. 2 Taibai South Road, 710071 Xi’an, China;2. Department of Applied Mathematics, Xi’an University of Architecture and Technology, 710055 Xi’an, China;1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, PR China;2. College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao 266590, Shandong, PR China;3. School of Mathematical Sciences, Liaocheng University, Liaocheng 252059, Shandong, PR China;4. School of Mechanical and Electrical Engineering, Suzhou University of Science and Technology, Suzhou 215000, Jiangsu, PR China;5. School of Science, Huzhou Teachers College, Huzhou 313000, Zhejiang, PR China |
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| Abstract: | This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. |
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