摘要
呈现了一类具有输出约束的未知非严格反馈系统的有限时间神经自适应跟踪控制。首先利用barrier Lyapunov函数、RBF(径向基函数)神经网络自适应、反步法以及有限时间控制理论设计出新颖的虚拟输入信号和实际输入信号,解决了非严格反馈系统的有限时间输出约束控制问题。其次,提出了具有输出约束的有限时间稳定性定理,确保上述所设计的控制器使得系统的输出能够在有限时间内跟踪上参考信号,同时也确保跟踪误差被约束在原点的小邻域内且闭环系统内的所有信号都有界。最后的物理仿真表明了所设计的控制器的有效性。因此,针对具有输出约束的非严格反馈系统设计的控制器具有良好的稳定性能和跟踪性能,为实际系统应用提供了理论支持。
This article presents a finite-time neural adaptive tracking control of an unknown non-strict feedback system with output constraints.Firstly,novel virtual input signals and actual input signals are designed by using Barrier Lyapunov function,RBF(radial basis function)neural network adaptation,backstepping,and finite-time control theory to solve the problem of finite-time output constraint control of non-strict feedback systems.Secondly,a finite time stability theorem with output constraints is proposed to ensure that the controller designed above enables the output of the system to track the reference signal in finite time,and that the tracking error is constrained in the small neighborhood of the origin and all signals are bounded in the closed-loop system.Finally,the physical simulation shows the effectiveness of the designed controller.Therefore,the controller designed for non-strict feedback systems with output constraints has good stability and tracking performance,which provides theoretical support for practical system applications.
作者
何诚
吴剑
张哲
HE Cheng;WU Jian;ZHANG Zhe(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China)
出处
《南昌航空大学学报（自然科学版）》
CAS
2020年第3期1-7,17,共8页
Journal of Nanchang Hangkong University(Natural Sciences)
基金
航空科学基金(2016ZC56003)
南昌航空大学研究生创新专项(YC2019026)
