Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation
文献类型：期刊论文
| 作者 | Liu, Kang5; Yang, Po5; Jiao, Lin4,6; Wang, Rujing2,6 ; Yuan, Zhipeng5; Li, Tao1,3
|
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2024 |
| 卷号 | 73 |
| 关键词 | Artificial neural networks Actuators Nonlinear systems Observers Adaptive systems Convergence Backstepping Actuator saturation finite-time control (FTC) full-state constraints neural networks (NNs) state observer |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2024.3370753 |
| 通讯作者 | Jiao, Lin(ljiao@ahu.edu.cn) ; Wang, Rujing(rjwang@iim.ac.cn) |
| 英文摘要 | This brief designs an observer-based adaptive finite-time neural control for a class of constrained nonlinear systems with external disturbances, and actuator saturation. First, a neural network (NN) state observer is developed to estimate the unmeasurable states. Combining the improved Gaussian function and an auxiliary compensation system (ACS), the actuator saturation can be solved. The "explosion of complexity" problem is tackled by the finite-time command filter (FTCF), and the filtering-error compensation system is constructed to resolve the filtering error. Moreover, the barrier Lyapunov function (BLF) is incorporated into the controller design to satisfy the state constraints. By integrating the NN technique and the virtual parameter learning to approximate the bound of the lumped disturbance, the number of learning parameters is decreased. It can be proved that all the states do not transgress the predefined bounds and the tracking errors converge to bounded regions in finite time. Eventually, we provide comparative results to show the feasibility of the obtained results. |
| WOS关键词 | TRACKING CONTROL ; NETWORK CONTROL ; STABILIZATION |
| 资助项目 | Innovate U.K. |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001181469700004 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Innovate U.K. |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136346]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Jiao, Lin; Wang, Rujing |
| 作者单位 | 1.Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China 2.Univ Sci & Technol China, Grad Sch, Isl Branch, Hefei 230031, Peoples R China 3.Hunan Univ Technol, Coll Railway Transportat, Zhuzhou 412007, Peoples R China 4.Anhui Univ, Sch InterNet, Hefei 230031, Peoples R China 5.Univ Sheffield, Dept Comp Sci, Sheffield S10 2TN, England 6.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Kang,Yang, Po,Jiao, Lin,et al. Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2024,73. |
| APA | Liu, Kang,Yang, Po,Jiao, Lin,Wang, Rujing,Yuan, Zhipeng,&Li, Tao.(2024).Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,73. |
| MLA | Liu, Kang,et al."Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。