College of Engineering, University of Baghdad, Baghdad 10017, Iraq.
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia.
Sensors (Basel). 2022 Aug 9;22(16):5935. doi: 10.3390/s22165935.
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.
采用自适应深度神经网络在逆系统辨识环境中逼近非线性对象的逆模型，目的是通过将收敛深度神经网络的权值和结构复制到后者来构成对象控制器。事实证明，这种深度学习（DL）方法在自适应逆控制（AIC）问题上优于自适应控制中通常使用的自适应滤波技术和算法，特别是在非线性对象中。控制器越深，逆函数逼近效果越好，前提是非线性对象具有逆模型且可以对其进行近似。仿真结果证明了这种基于 DL 的自适应逆控制方案的可行性。基于 DL 的 AIC 系统对非线性对象参数变化具有鲁棒性，因为对象输出重新获得参考信号的值的速度明显快于深度神经网络的自适应滤波器对应物。基于 DL 的 AIC 系统的阶跃响应的稳定时间和上升时间得到改善。
