24 citations · 40 across the 40 of their papers we have counts for
7 papers · 1 filter
Efficient sample selection for safe learning
Marta Zagorowska, Efe C. Balta, Varsha Behrunani +2
Ensuring safety in industrial control systems usually involves imposing constraints at the design stage of the control algorithm. Enforcing constraints is challenging if the underl…
Implications of Regret on Stability of Linear Dynamical Systems
Aren Karapetyan, Anastasios Tsiamis, Efe C. Balta +2
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for…
Risk-Averse Model Predictive Control for Priced Timed Automata
Mostafa Tavakkoli Anbarani, Efe C. Balta, Rômulo Meira-Góes +1
In this paper, we propose a Risk-Averse Priced Timed Automata (PTA) Model Predictive Control (MPC) framework to increase flexibility of cyber-physical systems. To improve flexibili…
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
Xavier Guidetti, Marino Kühne, Yannick Nagel +3
The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We u…
Controller-Aware Dynamic Network Management for Industry 4.0
Efe C. Balta, Mohammad H. Mamduhi, John Lygeros +1
In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected…
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
Efe C. Balta, Andrea Iannelli, Roy S. Smith +1
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the…
On Robustness in Optimization-Based Constrained Iterative Learning Control
Dominic Liao-McPherson, Efe C. Balta, Alisa Rupenyan +1
Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based IL…