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对于一类具有未知时变时滞和虚拟控制系数的不确定严格反馈非线性系统,基于后推设计提出一种自适应神经网络控制方案.选取适当的Lyapunov-Krasovskii泛函补偿未知时变时滞不确定项.通过构造连续的待逼近函数来解决利用神经网络对未知非线性函数进行逼近时出现的奇异问题.通过引入一个新的中间变量,保证了虚拟控制求导的正确性.仿真算例表明,所设计的控制器能保证闭环系统所有信号是半全局一致终结有界的,且跟踪误差收敛到零的一个邻域内.
For a class of uncertain strictly feedback nonlinear systems with unknown time-varying delays and virtual control coefficients, an adaptive neural network control scheme based on back-push design is proposed. An appropriate Lyapunov-Krasovskii functional to compensate unknown time-varying delays Uncertainties. By constructing continuous to-be-approximated functions to solve the singularity problem when using neural networks to approximate unknown nonlinear functions, the correctness of the derivation of virtual control is guaranteed by introducing a new intermediate variable. It is shown that the designed controller can ensure that all the signals of the closed-loop system are semi-globally uniformly terminated and the tracking error converges to a neighborhood of zero.