Finite-time adaptive neural prescribed tracking control of stochastic nonlinear systems with multiple power terms and unknown time-varying powers |
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| Institution: | 1. School of Automation and Electrical Engineering, Linyi University, Shandong Province, 276000, PR China;2. Institute of Intelligent Manufacturing, Shandong Traffic Technician College, Shandong Province, 276000, PR China;1. Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;2. Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany;3. Stanford Data Science, Stanford University, Stanford, CA, USA;4. Department of Psychology, Stanford University, Stanford, CA, USA;5. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK;6. Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany;7. BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany;8. Department of Artificial Intelligence, Korea University, Seoul, South Korea;9. Max Planck Institute for Informatics, Saarbrücken, Germany;1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Fars, Iran;2. Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Fars, Iran;1. Department of Mechanical Engineering, Politecnico di Milano, Italy;2. School of Automation, Nanjing University of Science and Technology, People''s Republic of China;3. The Adelaide University, Australia;4. Concordia University, Canada;1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, 110142, China;2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110169, China;3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China;4. University of Chinese Academy of Sciences, Beijing, 100049, China;5. The College of Automation, Shenyang Aerospace University, Shenyang, 110136, China |
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| Abstract: | This research addresses the problem of finite-time tracking error constrained control for a class of non-strict stochastic nonlinear systems with unknown time-varying powers and multiple power terms. Based on the conversion from constrained tracking error to an unconstrained signal with the same effect, by adopting the backstepping technique together with adaptive neural network control, a controller with upper and lower time-varying power bounds is designed to meet the prescribed performance control scheme in finite-time. Finally, two simulation examples are shown to verify the effectiveness of the commendatory control method. |
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