Stanford Seminar – Learning and Predictions in Autonomous Systems
Francesco Borrelli
UC Berkeley
October 25, 2019
Forecasts play an important role in autonomous and automated systems. Applications include transportation, energy, manufacturing and healthcare systems. 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. Our research over the past years has focused on predictive control design for autonomous systems performing iterative tasks. In this talk I will first provide an overview of the theory and tools that we have developed for the systematic design of learning predictive controllers. Then, I will focus on recent results on the use of data to efficiently formulate stochastic MPC problems which autonomously improve performance in iterative tasks. Throughout the talk I will focus on autonomous cars and solar power plants to motivate our research and show the benefits of the proposed techniques.
View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD"
Francesco Borrelli
UC Berkeley
October 25, 2019
Forecasts play an important role in autonomous and automated systems. Applications include transportation, energy, manufacturing and healthcare systems. 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. Our research over the past years has focused on predictive control design for autonomous systems performing iterative tasks. In this talk I will first provide an overview of the theory and tools that we have developed for the systematic design of learning predictive controllers. Then, I will focus on recent results on the use of data to efficiently formulate stochastic MPC problems which autonomously improve performance in iterative tasks. Throughout the talk I will focus on autonomous cars and solar power plants to motivate our research and show the benefits of the proposed techniques.
View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD”
