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SPOCK: A proximal method for multistage risk-averse optimal control problems
Alexander Bodard, Ruairi Moran, Mathijs Schuurmans +2
Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be…
Interaction-aware Model Predictive Control for Autonomous Driving
Renzi Wang, Mathijs Schuurmans, Panagiotis Patrinos
Lane changing and lane merging remains a challenging task for autonomous driving, due to the strong interaction between the controlled vehicle and the uncertain behavior of the sur…
Data-driven distributionally robust control of partially observable jump linear systems
Mathijs Schuurmans, Panagiotis Patrinos
We study safe, data-driven control of (Markov) jump linear systems with unknown transition probabilities, where both the discrete mode and the continuous state are to be inferred f…
Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility
Mathijs Schuurmans, Panagiotis Patrinos
We present a data-driven model predictive control scheme for chance-constrained Markovian switching systems with unknown switching probabilities. Using samples of the underlying Ma…
Risk-averse risk-constrained optimal control
Pantelis Sopasakis, Mathijs Schuurmans, Panagiotis Patrinos
Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate be…