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
收藏
页码：2773 / 2778
页数：6
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