Control systems are ubiquitous and enable the safe and predictable operation of airplanes, cars, and energy systems. Just as in other engineering disciplines, control engineers are interested in new possibilities AI offers to enhance traditional solutions. This talk will cover several areas where AI is gaining interest and adoption among control engineers and researchers.

We will first explore the use of AI for modeling the system to be controlled with techniques such as nonlinear system identification and reduced order modeling (ROM). We will also examine the benefits such ROMs offer in terms of speeding up simulations.

Next, we will discuss the use of AI for virtual sensor modeling and control algorithm design, in particular, the design of nonlinear model predictive control (MPC) using neural state-space (NSS) models. Obtaining a prediction model for MPC can be challenging in certain applications. In such cases, NSS models offer a viable alternative and can be trained using data collected from the system or a high-fidelity model. Additionally, we will touch upon how reinforcement learning (RL) can be used as a tool for controller tuning or can replace a traditional controller altogether. RL offers new possibilities such as using image-based observations and end-to-end solutions. Also, in cases where the action space is discrete, RL can avoid the need for solving challenging mixed integer programs online when compared to other optimal control techniques like MPC.

Despite the growing interest in using AI for controls, there remain several challenges, such as lack of performance guarantees in terms of stability, safety, etc. These can hinder widespread adoption in the industry. We will discuss some of the challenges we encountered based on our interactions with customers at MathWorks and introduce ideas such as constraint enforcement, tools, and best practices regarding control architecture to address these challenges.