Robust adaptive control of strict-feedback nonlinear systems with unmodelled dynamics and time-varying delays
Robust adaptive control of strict-feedback nonlinear systems with unmodelled dynamics and time-varying delays
复制标题
具有未建模动态和时变延迟的严格反馈非线性系统的鲁棒自适应控制
DOI：
10.1080/00207179.2016.1178810
复制
发表时间：
2017
影响因子：
2.1
通讯作者：
Chu Yuming
中科院分区：
文献类型：
--
作者：
Shi Xiaocheng;Xu Shengyuan;Li Yongmin;Chen Weimin;Chu Yuming
本文提出了一种新的鲁棒自适应神经网络控制方案，它可以作为自适应反推设计的鲁棒化。所考虑的一类不确定性包含未知的非对称死区输入，时变时滞不确定性，未知的动态干扰和未建模的动态。径向基函数神经网络（RBFNN）被用来逼近由Young不等式得到的未知非线性函数。通过构造指数型Lyapunov-Krasovskii泛函，补偿了时变时滞不确定性的上界函数。利用杨氏不等式和径向基函数神经网络，未建模动态的假设放宽。结果表明，所设计的控制器保证闭环系统中所有的信号是半全局一致最终有界的，并且跟踪误差最终收敛到零的一个邻域.
ABSTRACTThis paper presents a novel robust adaptive neural control scheme which can be taken as a robustification of the adaptive backstepping design. The considered class of uncertainties contains unknown non-symmetric dead-zone inputs, time-varying delay uncertainties, unknown dynamic disturbances and unmodelled dynamics. The radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions obtained by Young’s inequality. By constructing exponential Lyapunov-Krasovskii functionals, the upper bound functions of the time-varying delay uncertainties are compensated for. Using Young’s inequality and RBFNNs, the assumptions with respect to unmodelled dynamics are relaxed. It is demonstrated that the proposed controller guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error eventually converges to a neighbourhood of zero.