4 Ways to Improve Control Design Workflows with AI

Learn about the transformative potential of artificial intelligence (AI) for enhancing control system design and implementation. Hear how control engineers are using MATLAB^® and Simulink^® to integrate AI into their design and development processes, including:

• Plant modeling with techniques such as nonlinear system identification and reduced order modeling (ROM)
• Virtual sensor modeling and anomaly detection for estimating signals and identifying anomalies that are impractical or impossible to measure and identify using other techniques
• Design and implementation of data-driven control algorithms, including nonlinear model predictive control (MPC) using neural state-space (NSS) models, and reinforcement learning
• Productivity improvements using generative AI-powered capabilities of MATLAB Copilot 

In this session, you'll discuss the practical challenges and strategies for deploying AI models, particularly in embedded systems.