Deep Transfer Learning for Approximate Model Predictive Control

Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), where deep transfer learning is used to improve the training of the MPC by leveraging the knowledge gained from related controllers. One way in which transfer learning is applied in the context of MPC is by using a pre-trained deep learning model of the MPC, and then fine-tuning the controller training for a new process automation task. This is similar to how an equipment operator quickly learns to manually control a new processing unit because of related skills learned from controlling the prior unit. This reduces the amount of data required to train the approximate MPC controller, and also improves the performance on the target system. Additionally, learning the MPC actions alleviates the computational burden of online optimization calculations, although this approach is limited to learning from systems where an MPC has already been developed. The paper reviews approximate MPC formulations with a case study that illustrates the use of neural networks and transfer learning to create a multiple-input multiple-output (MIMO) approximate MPC. The performance of the resulting controller is similar to that of a controller trained on an existing MPC, but it requires less than a quarter of the target system data for training. The main contributions of this paper are a summary survey of approximate MPC formulations and a motivating case study that includes a discussion of future development work in this area. The case study presents an example of using neural networks and transfer learning to create a MIMO approximate MPC and discusses the potential for further research and development in this area. Overall, the goal of this paper is to provide an overview of the current state of research in approximate MPC, as well as to inspire and guide future work in transfer learning.