Predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the resulting system. However, constraint satisfaction, performance guarantees and real-time computation are challenged by the growing complexity of the engineered system, the human/machine interaction and the uncertainty of the environment where the system operates.

In this talk I will present the theory and tools that we have developed over the past ten years for the systematic design of predictive controllers for uncertain linear and nonlinear systems. I will first provide an overview of our theoretical efforts. Then, I will focus on our recent results for addressing constraints satisfaction in large-scale networked systems and real-time computation by using analog optimization.

Throughout the talk I will use two applications to motivate our research and show the benefits of the proposed techniques: Safe Autonomous Cars and Green Intelligent Buildings.

Host: Misha Chertkov