Approximate Optimal Indirect Regulation of an Unknown Agent With a Lyapunov-Based Deep Neural Network

被引：3

作者：

Makumi, Wanjiku A. ^{[1
]}

Bell, Zachary I. ^{[2
]}

Dixon, Warren E. ^{[1
]}

机构：

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

[2] Air Force Res Lab, Munit Directorate, Eglin AFB, FL 32542 USA

来源：

IEEE CONTROL SYSTEMS LETTERS | 2023年 / 7卷

关键词：

Deep learning; System identification; Function approximation; Real-time systems; Optimal control; Artificial neural networks; Aerodynamics; Deep neural networks; reinforcement learning; adaptive control; Lyapunov methods; nonlinear control systems; MULTIPLE TARGETS; TIME;

D O I：

10.1109/LCSYS.2023.3289474

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An approximate optimal policy is developed for a pursuing agent to indirectly regulate an evading agent coupled by an unknown interaction dynamic. Approximate dynamic programming is used to design a controller for the pursuing agent to optimally influence the evading agent to a goal location. Since the interaction dynamic between the agents is unknown, integral concurrent learning is used to update a Lyapunov-based deep neural network to facilitate sustained learning and system identification. A Lyapunov-based stability analysis is used to show uniformly ultimately bounded convergence. Simulation results demonstrate the performance of the developed method.

引用

页码：2773 / 2778

页数：6

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