Deep Learning Enabled Maintenance Free Control

现代工业厂房是高度集成和复杂的。因此，有几种常用的控制器层。这些包括简单的单回路PID控制器，多变量模型预测控制器（MPC）和实时优化器。这些控制器依赖于一些调优参数，这些参数反过来又决定了整体闭环性能。调优这些参数本身就是一项困难的工作，因为有几个调优参数，它们通常不适合可靠的优化公式。此外，调优通常是资源密集型的、昂贵的和耗时的。

Modern industrial plants are highly integrated and complex. As such there are several layers of controllers that are commonly used. These include simple single loop PID controllers, multivariable Model Predictive Controllers (MPC), and Real Time Optimizers. These controllers rely on a number of tuning parameters that in turn dictate the overall closed loop performance. Tuning these parameters is an inherently difficult exercise as there are several tuning parameters and they are often not amenable to reliable optimization formulations. In addition, tuning often is resource intensive, expensive and time consuming. Recently, several longstanding problems in computer science have been solved using ideas from dynamic programming and artificial neural networks. The common theme across the solutions developed for these problems is the superior performance of Deep Neural Networks (DNNs) in approximation complex functions and the automatic learning ability of reinforcement learning (RL) algorithms. In this project, we will extended the successes of DNN and RL to automatically tune PID, MPC and other nonlinear controllers.