Modeling Autonomous Systems: Theory and Tools

This lecture delves into the importance of modeling and predicting uncertain environments for ensuring safe and high-performance autonomy in modern autonomous systems. The instructor, Prof. Francesco Borrelli from UC Berkeley, presents research focusing on control design for autonomous systems integrating predictions and learning while ensuring safety. The lecture covers the comparison with model-free approaches and discusses key open questions in the field. Topics include model-based predictive control, AI/ML, disciplined control design, offline and online computation, feedback policies, and addressing model mismatch. The presentation also touches on challenges and solutions for implementing predictive control in neuroscience and showcases real-world applications and experiments.