Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance |
| |
| Institution: | 1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, PR China;2. Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo 36000, Serbia;1. Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, China;1. School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350108, China;2. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China |
| |
| Abstract: | This article investigates the adaptive neural network fixed-time tracking control issue for a class of strict-feedback nonlinear systems with prescribed performance demands, in which the radial basis function neural networks (RBFNNs) are utilized to approximate the unknown items. First, an modified fractional-order command filtered backstepping (FOCFB) control technique is incorporated to address the issue of the iterative derivation and remove the impact of filtering errors, where a fractional-order filter is adopted to improve the filter performance. Furthermore, an event-driven-based fixed-time adaptive controller is constructed to reduce the communication burden while excluding the Zeno-behavior. Stability results prove that the designed controller not only guarantees all the signals of the closed-loop system (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to the predefined boundary. Finally, the feasibility and superiority of the proposed control algorithm are verified by two simulation examples. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
