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提出一类不依赖于模型的状态观测器,通过分析其根轨迹和极点要求配置合适的参数,该观测器本身是一个能提取高阶微分的高阶微分器.基于Lyapunov稳定性理论设计了使闭环系统渐近稳定,对模型变化和扰动具有鲁棒性的神经网络自适应控制器.该控制器不仅考虑了闭环系统的输出和设定输入误差的微分,而且考虑了误差的高阶微分,从而提高了控制品质.最后通过仿真例子验证了所提出理论的正确性.
A class of model-independent state observer is proposed, and the proper parameters are configured by analyzing its root locus and pole requirements. The observer itself is a high-order differentiator which can extract higher-order differentials. Based on Lyapunov stability theory, The closed-loop system is asymptotically stable and has robustness to model changes and disturbances. The controller not only considers the output of the closed-loop system and the differential of the set input error, but also considers the high-order differential of the error, Thereby improving the quality of control.Finally, the simulation results verify the validity of the proposed theory.