基于神经网络控制性能评估的扩展状态观测器整定
Tuning of extended state observer with neural network-based control performance assessment
摘要：
扩张状态观测器是鲁棒扰动控制系统的一个固有环节，它可以估计扰动对系统动态特性的影响。ESO参数的适当调整是必要的，以确保一个良好的质量估计量和影响的整体性能的鲁棒控制结构。在本文中，我们提出了一个神经网络（NN）为基础的调整程序，允许用户之间的优先级选择的质量标准，如控制和观测误差和控制信号的指定功能。所设计的神经网络提供了一个精确的评估的控制系统的性能，并返回一组ESO参数，提供了一个接近最优的解决方案，用户定义的代价函数。建议的调整程序，使用估计状态从单闭环实验产生近最佳ESO增益在几秒钟内。
英文摘要：
The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
