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针对一类复杂非线性动力学系统 ,提出一种基于神经网络动态补偿的模型跟随非线性鲁棒自适应控制策略 .采用神经网络在线补偿控制器以克服系统的未建模动力学和非线性耦合因素的影响 ,从而提高了模型跟随控制的动态性能和稳态精度 ;当系统存在模型不确定性和外部扰动时 ,其输出仍能精确地跟踪期望参考模型的输出 .同时给出了闭环误差系统鲁棒稳定性的证明 .应用示例表明 ,所提方法可保证闭环系统具有良好的跟踪性能和鲁棒性 ,且算法简单 ,易于在线控制 .
For a class of complex nonlinear dynamic systems, a model-following nonlinear robust adaptive control strategy based on neural network dynamic compensation is proposed.The neural network online compensation controller is adopted to overcome the unmodeled dynamics and nonlinear coupling of the system Which can improve the dynamic performance and steady-state accuracy of the model-following control.When the system has model uncertainty and external disturbance, the output of the system can still accurately track the output of the expected reference model.At the same time, the closed-loop error system Proof of robust stability. The application examples show that the proposed method can ensure the closed-loop system has good tracking performance and robustness, and the algorithm is simple and easy to online control.