Grant: Collaborative Research and Development Grants

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.