3 citations · 3 across the 1 of their papers we have counts for
8 papers · 1 filter
Learning -function approximations for hybrid control problems
Sandeep Menta, Joseph Warrington, John Lygeros +1
The main challenge in controlling hybrid systems arises from having to consider an exponential number of sequences of future modes to make good long-term decisions. Model predictiv…
Reliable Power Grid: Long Overdue Alternatives to Surge Pricing
Hala Ballouz, Joel Mathias, Sean Meyn +2
This paper takes a fresh look at the economic theory that is motivation for pricing models, such as critical peak pricing (CPP), or surge pricing, and the demand response models ad…
Learning continuous Q-Functions using generalized Benders cuts
Joseph Warrington
Q-functions are widely used in discrete-time learning and control to model future costs arising from a given control policy, when the initial state and input are given. Although so…
Two-Stage Dual Dynamic Programming with Application to Nonlinear Hydro Scheduling
Benjamin Flamm, Annika Eichler, Joseph Warrington +1
We present an approximate method for solving nonlinear control problems over long time horizons, in which the full nonlinear model is preserved over an initial part of the horizon,…
Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems
Joseph Warrington, Dominik Ruchti
Mobility systems featuring shared vehicles are often unable to serve all potential customers, as the distribution of demand does not coincide with the positions of vehicles at any…
A Two-Stage Polynomial Approach to Stochastic Optimization of District Heating Networks
Marc Hohmann, Joseph Warrington, John Lygeros
In this paper, we use stochastic polynomial optimization to derive high-performance operating strategies for heating networks with uncertain or variable demand. The heat flow in di…