论文标题
使用神经网络为网络物理系统建模控制器
Modelling Controllers for Cyber Physical Systems Using Neural Networks
论文作者
论文摘要
模型预测控制器（MPC）广泛用于控制网络物理系统。这是一个在固定时间范围内优化机器人未来状态的预测的迭代过程。 MPC在实践中是有效的，但是由于它们在计算上昂贵且缓慢，因此它们不适合用于实时应用程序。可以通过近似MPC的功能来克服缺陷。神经网络是非常好的功能近似器，与MPC相比更快。将神经网络应用于基于控制的应用程序可能会很具有挑战性，因为数据与I.I.D假设不符。这项研究调查了在基于控制的环境中使用神经网络的各种模仿学习方法，并评估了其益处和缺点。
Model Predictive Controllers (MPC) are widely used for controlling cyber-physical systems. It is an iterative process of optimizing the prediction of the future states of a robot over a fixed time horizon. MPCs are effective in practice, but because they are computationally expensive and slow, they are not well suited for use in real-time applications. Overcoming the flaw can be accomplished by approximating an MPC's functionality. Neural networks are very good function approximators and are faster compared to an MPC. It can be challenging to apply neural networks to control-based applications since the data does not match the i.i.d assumption. This study investigates various imitation learning methods for using a neural network in a control-based environment and evaluates their benefits and shortcomings.
