Model Predictive Control for Systems with Partially Unknown Dynamics under Signal Temporal Logic Specifications

被引：0
|
作者：
Dai, Zhao Feng [1 ]
Pant, Yash Vardhan [1 ]
Smith, Stephen L. [1 ]
机构：
[1] University of Waterloo, Department of Electrical and Computer Engineering, Waterloo,ON,N2L 3G1, Canada
来源：
IEEE Control Systems Letters | 2024年 / 8卷
关键词：
In this letter; we design a model predictive controller (MPC) for systems to satisfy Signal Temporal Logic (STL) specifications when the system dynamics are partially unknown; and only a nominal model and past runtime data are available. Our approach uses Gaussian process regression to learn a stochastic; data-driven model of the unknown dynamics; and manages uncertainty in the STL specification resulting from the stochastic model using Probabilistic Signal Temporal Logic (PrSTL). The learned model and PrSTL specification are then used to formulate a chance-constrained MPC. For systems with high control rates; we discuss a modification for improving the solution speed of the control optimization. In simulation case studies; our controller increases the frequency of satisfying the STL specification compared to controllers that use only the nominal dynamics model. © 2017 IEEE;
D O I：
10.1109/LCSYS.2024.3519034
中图分类号：
学科分类号：
摘要：
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页码：2931 / 2936
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