Approximate real-time optimal control based on sparse Gaussian process models
Approximate real-time optimal control based on sparse Gaussian process models
复制标题
基于稀疏高斯过程模型的近似实时最优控制
DOI：
10.1109/adprl.2014.7010608
复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Riedmiller
中科院分区：
文献类型：
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作者：
J. Boedecker;J. T. Springenberg;J. Wülfing;M. Riedmiller
在本文中，我们提出了一个全自动的方法（近似）非线性系统的最优控制。我们的算法共同学习系统动态的非参数模型-基于高斯过程回归（GPR）-并使用自适应迭代LQR制定进行滚动时域控制。这导致了一种非常有效的数据学习算法，可以在实时约束下运行。结合探地雷达方差的探索策略，我们的算法成功地学会控制两个基准问题的模拟（两连杆机械手，推车杆），以及摆动和平衡的真实的推车杆系统。对于所有考虑的问题从头开始学习，即没有专家提供的先验知识，成功地与系统进行了不到10次交互。
In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model of the system dynamics - based on Gaussian Process Regression (GPR) - and performs receding horizon control using an adapted iterative LQR formulation. This results in an extremely data-efficient learning algorithm that can operate under real-time constraints. When combined with an exploration strategy based on GPR variance, our algorithm successfully learns to control two benchmark problems in simulation (two-link manipulator, cart-pole) as well as to swing-up and balance a real cart-pole system. For all considered problems learning from scratch, that is without prior knowledge provided by an expert, succeeds in less than 10 episodes of interaction with the system.
自适应内部动力学模型的最优控制
DOI：
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发表时间：
2008
期刊：
ICINCO-ICSO
影响因子：
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作者：
Djordje Mitrović;Stefan Klanke;S. Vijayakumar
通讯作者：
S. Vijayakumar