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Learning Self-Triggered Controllers With Gaussian Processes
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DOI：10.1109/TCYB.2020.2980048.png)
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Abstract
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.
Keywords:
Optimal control
Heuristic algorithms
Gaussian processes
Vehicle dynamics
Approximation algorithms
Kernel
Mathematical model
Event-triggered
self-triggered control
Gaussian process (GP) regression
optimal control
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W