.png){kind=icon}
返回
Learning Self-Triggered Controllers With Gaussian Processes
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2020.2980048.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown transition dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. Moreover, we provide an overall implementation algorithm that jointly learns the dynamics of the plant and the self-triggered controller based on a reinforcement learning framework. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.
Keyword:
Optimal control
Heuristic algorithms
Gaussian processes
Vehicle dynamics
Approximation algorithms
Kernel
Mathematical model
Event-triggered
self-triggered control
Gaussian process (GP) regression
optimal control
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
Codesign of Event Trigger and Feedback Policy in Robust Model Predictive Control鲁棒模型预测控制中事件触发与反馈策略的协同设计
PREAI
分享收藏Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection基于自适应控制样本选择的非线性输入仿射动态系统自触发模型预测控制
OAAI
OAAI分享收藏Robust event-triggered model predictive control for cyber-physical systems under denial-of-service attacks拒绝服务攻击下网络物理系统的鲁棒事件触发模型预测控制
PREAI
分享收藏
查看更多内容