Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems

被引：20

作者：

Le, Duc M. ^{[1
]}

Greene, Max L. ^{[1
]}

Makumi, Wanjiku A. ^{[1
]}

Dixon, Warren E. ^{[1
]}

机构：

[1] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL 32611 USA

来源：

IEEE CONTROL SYSTEMS LETTERS | 2022年 / 6卷

关键词：

Real-time systems; Trajectory; Switches; Feedforward systems; Adaptive control; Training data; Training; deep neural networks; Lyapunov methods; nonlinear control systems; ROBOT;

D O I：

10.1109/LCSYS.2021.3081361

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A real-time deep neural network (DNN) adaptive control architecture is developed for uncertain control-affine nonlinear systems to track a time-varying desired trajectory. A Lyapunov-based analysis is used to develop adaptation laws for the output-layer weights and develop constraints for inner-layer weight adaptation laws. Unlike existing works in neural network and DNN-based control, the developed method establishes a framework to simultaneously update the weights of multiple layers for a DNN of arbitrary depth in real-time. The real-time controller and weight update laws enable the system to track a time-varying trajectory while compensating for unknown drift dynamics and parametric DNN uncertainties. A nonsmooth Lyapunov-based analysis is used to guarantee semi-global asymptotic tracking. Comparative numerical simulation results are included to demonstrate the efficacy of the developed method.

引用

页码：476 / 481

页数：6