21 citations · 34 across the 47 of their papers we have counts for
12 papers · 1 filter
SPPID: Saddle-Point PID for Constrained Optimization
Veronica Centorrino, Rawan Hoteit, Efe C. Balta +1
This paper studies constrained optimization problems through the lens of feedback control. Building on the interpretation of Lagrange multipliers as feedback controllers, we propos…
A Unified Control-Theoretic Framework for Saddle-Point Dynamics in Constrained Optimization
Veronica Centorrino, Rawan Hoteit, Efe C. Balta +1
This paper studies equality-constrained minimization problems through the lens of feedback control. We introduce a unified control-theoretic framework by showing that a PID feedbac…
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…
Operator Splitting for Convex Constrained Markov Decision Processes
Panagiotis D. Grontas, Anastasios Tsiamis, John Lygeros
We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers,…
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter +2
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from obs…
Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing
Xavier Guidetti, Efe C. Balta, John Lygeros
In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to s…
Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Efe Balta +2
We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is…
Data-Driven Extrusion Force Control Tuning for 3D Printing
Xavier Guidetti, Ankita Mukne, Marvin Rueppel +3
The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods i…
BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function and the constraints of the MPC to maximize closed-loop performance remain…
Convergence guarantees for adaptive model predictive control with kinky inference
Riccardo Zuliani, Raffaele Soloperto, John Lygeros
We analyze the convergence properties of a robust adaptive model predictive control algorithm used to control an unknown nonlinear system. We show that by employing a standard quad…
Computing Optimal Joint Chance Constrained Control Policies
Niklas Schmid, Marta Fochesato, Sarah H. Q. Li +2
We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard Dynamic P…
Interconnection of (Q,S,R)-Dissipative Systems in Discrete Time
Andrea Martinelli, Ahmed Aboudonia, John Lygeros
Discrete-time systems cannot be passive unless there is a direct feedthrough from the input to the output. For passivity-based control to be exploited nevertheless, some authors in…