An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.

中文翻译：

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非线性动态系统鲁棒自适应逆控制的深度学习：使用自动编码器改进稳定时间

自适应深度神经网络用于逆系统识别设置，以逼近非线性对象的逆，目的是通过将收敛深度神经网络的权重和架构复制到后者来构成对象控制器。这种针对自适应逆向控制 (AIC) 问题的深度学习 (DL) 方法被证明优于自适应控制中通常使用的自适应滤波技术和算法，尤其是在非线性设备中。控制器越深，反函数逼近越好，前提是非线性被控对象具有逆并且该逆可以被逼近。仿真结果证明了这种基于深度学习的自适应逆控制方案的可行性。基于 DL 的 AIC 系统对非线性受控对象参数变化具有鲁棒性，因为受控对象输出重新假定参考信号的值比使用深度神经网络的自适应滤波器对应物要快得多。阶跃响应的建立和上升时间在基于 DL 的 AIC 系统中得到改善。