Development and Deployment of AI Algorithms in Control Units Using MATLAB and Simulink

See a comprehensive approach to accelerating the development and deployment of AI and machine learning algorithms in embedded control units using MATLAB^® and Simulink^®. By leveraging Model-Based Design principles, discover how end-to-end development pipelines can be derived and deployed efficiently to target microcontrollers in production programs. Integrating MATLAB and Simulink with cloud platforms, particularly AWS^®, enables scalable simulation, rapid calibration, and benchmarking of machine learning algorithms for highly nonlinear systems.

We showcase multiple real-world applications where AI integration was made practical with MATLAB and Simulink, ranging from control logic optimization to predictive diagnostics, and we highlight how these tools expedite the realization of concepts into deployable systems. Learn how these pipelines seamlessly integrate into Agile workflows, facilitating iterative development, validation, and deployment cycles. You’ll also explore the use of these pipelines in software-defined vehicles (SDVs), providing a foundation for robust machine learning lifecycle management in production environments.

Key benefits include simplified workflow setup, effective failure-mode testing, rapid deployment of high-fidelity models, and reduced dependency on physical testbenches through cloud-based simulation. There are also practical challenges such as limited workflow expertise, managing black-box recalibration, and the manual oversight required for training data validation. This work provides insights and best practices to system developers aiming to adopt AI and machine learning within embedded systems using MATLAB and Simulink and cloud infrastructure.