Intelligent systems are becoming increasingly prevalent in our society, e.g., self-driving cars are being developed on an industrial scale and smart grids are at the forefront of efforts to combat climate change. Model predictive control (MPC), a powerful optimization-based constrained control technique, is a key enabling technology for the next generation of intelligent systems. Often the most significant challenge when deploying model predictive controllers is solving complex trajectory optimization problems in real-time. It is often not possible to solve these problems to full optimality in practice due to computational limits, instead we resort to computationally cheaper suboptimal predictive control strategies. However, this potentially sacrifices some of MPC’s stability and robustness guarantees. In this talk, I present Time-distributed Optimization (TDO), a unifying framework for studying the system theoretic consequences of computational limits in the context of Model Predictive Control (MPC). By framing suboptimal MPC as a feedback interconnection between the physical plant and an optimization algorithm, I derive sufficient conditions for stability and robustness of model predictive controllers under computing power and/or communication limits. Further, I illustrate the applicability of the these methods in the real-world through diesel engine, and autonomous driving examples. Speaker(s): Dominic Liao-McPherson Agenda: The event takes place on Wednesday Feb 14th from 5:30pm to 7:00pm. 5:30pm Start – Gathering – Introduction 5:40pm – Talk – Discussion 7:00PM End Speaker: Dominic Moderator: Dejan University of British Columbia , Vancouver , British Columbia, Canada, Virtual: https://events.vtools.ieee.org/m/401418