Adaptive Neural Dynamic Surface Control of Pure-Feedback Nonlinear Systems With Full State Constraints and Dynamic Uncertainties
Adaptive Neural Dynamic Surface Control of Pure-Feedback Nonlinear Systems With Full State Constraints and Dynamic Uncertainties
复制标题
具有全状态约束和动态不确定性的纯反馈非线性系统的自适应神经动态表面控制
DOI：
10.1109/tsmc.2017.2675540
复制
发表时间：
2017
影响因子：
8.7
通讯作者：
Shen Qikun
中科院分区：
文献类型：
--
作者：
Zhang Tianping;Xia Meizhen;Yi Yang;Shen Qikun
针对一类具有完全状态约束和动态不确定性的纯反馈非线性系统，提出了一种基于径向基函数神经网络的自适应神经动态面控制方法。基于一对一的非线性映射，将具有完全状态约束的纯反馈系统转化为一种新的无状态约束的纯反馈系统。动态不确定性处理使用动态信号。利用改进的DSC和中值定理以及Nussbaum函数，提出了两种基于变换系统的自适应神经网络控制方案。所设计的控制策略消除了控制增益上界已知的条件，虚拟控制系数的上界和下界也是已知的。证明了闭环系统的所有信号都是半全局一致最终有界的，并且不违反全状态约束。两个数值例子说明了所提出的方法的有效性。
In this paper, adaptive neural dynamic surface control (DSC) is developed using radial basis function neural networks (NNs) for a class of pure-feedback nonlinear systems with full state constraints and dynamic uncertainties. Based on a one-to-one nonlinear mapping, the pure-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. The dynamic uncertainties are dealt with using a dynamic signal. Using modified DSC and mean value theorem as well as Nussbaum function, two adaptive NN control schemes are proposed based on the transformed system. The designed control strategy removes the conditions that the upper bound of the control gain is known, and the lower bounds and upper bounds of the virtual control coefficients are known. It is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the full state constraints are not violated. Two numerical examples are provided to illustrate the effectiveness of the proposed approach.