基于ADP的非对称约束系统动态事件触发方法

薛珊; 赵宁; 张卫东

doi:10.16383/j.aas.c250544

基于ADP的非对称约束系统动态事件触发方法

doi: 10.16383/j.aas.c250544 cstr: 32138.14.j.aas.c250544

薛珊^{1, 2, 3,},
赵宁^{1, 3,},
张卫东^{1, 3, 4,}

1.
海南大学信息与通信工程学院海口 570228
2.
安徽大学人工智能学院合肥 230601
3.
海洋智能系统教育部工程研究中心海口 570228
4.
上海交通大学自动化与智能感知学院上海 200240

基金项目: 国家自然科学基金(U2141234, U24A20260, 62403173), 海南省自然科学基金(725RC724), 海南省高等教育科研资助项目(Hnky2025ZD-2), 中国国家科技重大专项(2022ZD0119900), 海南省科技专项(ZDYF2024GXJS003), 海南大学科研基金(XJ2400000440)资助

详细信息

作者简介:
薛珊：海南大学信息与通信工程学院副教授. 2023年获得华南理工大学计算机科学与工程学院博士学位. 主要研究方向为人工智能理论, 智能控制理论, 具身智能机器人. E-mail: xueshan@hainanu.edu.cn

赵宁：海南大学信息与通信工程学院博士研究生. 主要研究方向为多智能体系统, 强化学习, 自适应动态规划, 海洋智能系统. E-mail: znning@hainanu.edu.cn

张卫东：海南大学信息与通信工程学院教授, 上海交通大学特聘教授. 1990年、1993年和1996年分别获得浙江大学学士、硕士和博士学位. 主要研究方向为控制理论, 机器学习理论及其在工业和机器人中的应用. 本文通信作者. E-mail: wdzhang@sjtu.edu.cn

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出版历程
- 收稿日期: 2025-10-14
- 录用日期: 2025-12-19
- 网络出版日期: 2026-04-17
- 刊出日期: 2026-06-20

ADP-based Dynamic Event-triggering Method for Asymmetric Constrained Systems

XUE Shan^{1, 2, 3
,},
ZHAO Ning^{1, 3
,},
ZHANG Wei-Dong^{1, 3, 4
,}

1.
School of Information and Communication Engineering, Hainan University, Haikou 570228
2.
School of Artificial Intelligence, Anhui University, Hefei 230601
3.
Engineering Research Center of Marine Intelligent Systems, Ministry of Educa-tion, Haikou 570228
4.
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240

Funds: Supported by National Natural Science Foundation of China (U2141234, U24A20260, 62403173), Hainan Provincial Natural Science Foundation (725RC724), Hainan Higher Education Scientific Research Funding Project (Hnky2025ZD-2), National Science and Technology Major Project of China (2022ZD0119900), Hainan Provincial Science and Technology Special Fund (ZDYF2024GXJS003), and Scientific Research Fund of Hainan University (XJ2400000440)

More Information

Author Bio:
XUE Shan　Associate professor at the School of Information and Communication Engineering, Hainan University. She received her Ph.D. degree from the School of Computer Science and Engineering, South China University of Technology in 2023. Her research interests include theory of artificial intelligence, intelligent control theory, embodied intelligent robot

ZHAO Ning　Ph.D. candidate at the School of Information and Communication Engineering, Hainan University. His research interests include multi-agent systems, reinforcement learning, adaptive dynamic programming, and intelligent marine systems

ZHANG Wei-Dong　Professor at the School of Information and Communication Engineering, Hainan University, and distinguished professor at the Shanghai Jiao Tong University. He received his bachelor, master and Ph.D. degrees from Zhejiang University in 1990, 1993 and 1996, respectively. His research interests include control theory, machine learning theory, and their applications in industry and robotics. Corresponding author of this paper

摘要

摘要: 本文提出一种基于自适应动态规划的动态事件触发方法(DEM), 用于解决具有状态与控制双重非对称约束的非线性连续时间系统最优控制问题. 首先, 利用非线性映射函数将非对称约束系统的控制问题转化为无约束形式. 然后, 设计一种静态事件触发方法(SEM), 其中触发条件仅与当前状态相关. 进一步, 开发一种依赖额外内部动态变量的DEM, 其触发条件也与系统历史信息相关. 事实上, DEM是SEM的进阶方法. 理论分析证实DEM在确保系统性能的情况下, 能够进一步节省计算和网络资源. 最后, 介绍基于神经网络的实现方法. 在无人水面艇仿真实验环境下, 该方法的有效性得到了验证.
- 自适应动态规划 /
- 自适应评价设计 /
- 神经网络 /
- 事件触发方法 /
- 动态事件触发方法
Abstract: In this paper, an adaptive dynamic programming-based dynamic event-triggering method (DEM) is developed to solve the optimal control problem of nonlinear continuous-time systems with asymmetric constraints for both state and control. First, a nonlinear mapping function is used to transform the control problem of asymmetric constrained systems into an unconstrained form. Then, a static event-triggering method (SEM) is designed, where triggering conditions are only associated with the current state. Based on the SEM, a DEM that relies on an additional internal dynamic variable is developed, whose triggering condition is also related to the system historical information. In fact, the DEM is an advanced method of the SEM. Theoretical analysis proves that the DEM can further save computational and network resources while ensuring system performance. Finally, the neural network-based implementation is presented. The effectiveness of this method has been verified in the simulation experiment environment of the unmanned surface vehicle.
- adaptive dynamic programming /
- adaptive critic designs /
- neural networks /
- event-triggering method /
- dynamic event-triggering method

HTML全文

图 1 USV示意图

Fig. 1 The schematic diagram of USV

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图 2 ETM中$ \hat{w} $的演化

Fig. 2 Evolution of $ \hat{w} $ in ETM

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图 3 控制策略的演化

Fig. 3 Evolution of control policy

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图 4 ETM的轨迹

Fig. 4 Trajectory of ETM

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图 5 执行间隔时间的轨迹

Fig. 5 Trajectory of the interexecution time

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图 6 样本的演化

Fig. 6 Evolution of samples

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图 7 无映射下$ s(t) $的轨迹

Fig. 7 Trajectory of $ s(t) $ without mapping

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图 8 有映射下$ s(t) $的轨迹

Fig. 8 Trajectory of $ s(t) $ with mapping

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表 1 命名法

Table 1 Nomenclature

符号	含义
$ {\bf{N}} $	正整数集合
$ {\bf{R}} $	实数集合
$ {\bf{R}}^{m} $	$ m $ 维欧氏空间
$ {\bf{R}}^{m\times n} $	$ m\times n $ 维矩阵空间
$ \text{T} $	转置
$ \nabla J(s) $	梯度算子
$ \\| \cdot \\| $	2范数

下载: 导出CSV

表 2 TTM, SEM和DEM的比较

Table 2 Comparison of TTM, SEM and DEM

方法	EOC	EVT	AET	NOS	NOA
TTM^[9]	连续	—	0.01	1500	1500
SEM^[24]	分割	否	0.04	368	1500
DEM	分割	否	0.07	229	0

下载: 导出CSV

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