Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics
文献类型：期刊论文
| 作者 | Zhao XG(赵新刚)1 ; Yang, Haijiao2; Cai, Yujin2; Ye D(叶丹)2
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| 刊名 | Nonlinear Dynamics
 |
| 出版日期 | 2019 |
| 卷号 | 97期号:1页码:715-732 |
| 关键词 | Non-strict feedback Adaptive control Full-state constraints Unmodeled dynamics Input saturation |
| ISSN号 | 0924-090X |
| 产权排序 | 2 |
| 英文摘要 | In this paper, an adaptive neural network controller is designed for non-strict feedback systems with full-state constraints. According to practical applications, both input saturation and unmodeled dynamics are also taken into account. By using a logarithm nonlinear mapping, non-strict feedback systems with full-state constraints can be converted to unconstrained ones, which may result in some exponential terms. Here, a new variable separation method is proposed based on Taylor’s formula to cope with the exponential terms and non-strict structure. Then, the relationship between the norm of state vector and error functions is established. A hyperbolic tangent function and a dynamic signal are introduced to deal with input saturation and unmodeled dynamics, respectively. It is proved that all signals of the closed-loop system are uniformly ultimately bounded and the requirement of full-state constraints is satisfied. Two illustrative examples are provided to demonstrate the effectiveness of the presented method. |
| WOS关键词 | BARRIER LYAPUNOV FUNCTIONS ; NONLINEAR-SYSTEMS ; NETWORK CONTROL ; FUZZY CONTROL |
| 资助项目 | National Natural Science Foundation of China[61773097] ; National Natural Science Foundation of China[U1813214] ; Fundamental Research Funds for the Central Universities[N160402004] |
| WOS研究方向 | Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:000473520700044 |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.sia.cn/handle/173321/25324]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Zhao XG(赵新刚) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang 110016, China 2.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China |
| 推荐引用方式 GB/T 7714 | Zhao XG,Yang, Haijiao,Cai, Yujin,et al. Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics[J]. Nonlinear Dynamics,2019,97(1):715-732. |
| APA | Zhao XG,Yang, Haijiao,Cai, Yujin,&Ye D.(2019).Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics.Nonlinear Dynamics,97(1),715-732. |
| MLA | Zhao XG,et al."Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics".Nonlinear Dynamics 97.1(2019):715-732. |
入库方式： OAI收割
来源：沈阳自动化研究所
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