Feedback Control by Online Learning an Inverse Model
Feedback Control by Online Learning an Inverse Model
复制标题
通过在线学习逆模型进行反馈控制
DOI：
--
复制
发表时间：
2012
影响因子：
10.4
通讯作者：
B. Schrauwen
中科院分区：
文献类型：
--
作者：
Tim Waegeman;F. Wyffels;B. Schrauwen
反馈控制器经常使用模型、预测器或误差估计器来控制对象。当对象表现出非线性行为时，创建这样的模型是困难的。本文提出了一种新的在线学习控制框架，它不需要对被控对象有明确的了解。该框架使用两个学习模块，一个用于创建逆模型，另一个用于实际控制对象。除了它们的输入之外，它们是相同的。逆模型通过尚未完全训练的控制器执行的探索来学习，而实际控制器基于当前学习的模型。所提出的框架允许快速在线学习精确的控制器。该控制器可广泛应用于具有不同动态特性的任务。我们通过将我们的控制框架应用于几个控制任务来验证这一说法：1)加热箱问题(慢非线性动力学)；2)飞行俯仰控制(慢线性动力学)；以及3)二级倒立摆的平衡问题(快线性和非线性动力学)。实验结果表明，该方法学习速度快，控制精度高。此外，还与一些经典的控制方法进行了比较，并对收敛和稳定性进行了观察。
A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made.