8 papers
MPC and System Identification with Differentiable Physics: Fluid System and Particle Beam Control
Alan Williams, Alp Sunol
We consider the problem of simultaneous control and parameter estimation when the model is available only as a differentiable physics simulator. We propose a receding-horizon contr…
Nested Extremum Seeking Converges to Stackelberg Equilibrium
Brad Ratto, Alan Williams, Miroslav KrstiÄ +2
The nested Extremum Seeking (nES) algorithm is a model-free optimization method that has been shown to converge to a neighborhood of a Nash equilibrium. In this work, we demonstrat…
Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking
Shaifalee Saxena, Alan Williams, Rafael Fierro +1
In this paper, we study the use of robust model independent bounded extremum seeking (ES) feedback control to improve the robustness of deep reinforcement learning (DRL) controller…
Generalized Multi-Constraint Extremum Seeking
Alan Williams, Jorge Cortés, Alexander Scheinker
We generalize the Safe Extremum Seeking algorithm to address the minimization of an unknown objective function subject to multiple unknown inequality and equality constraints, rely…
Adaptive conditional latent diffusion maps beam loss to 2D phase space projections
Alexander Scheinker, Alan Williams
Beam loss (BLM) and beam current monitors (BCM) are ubiquitous at particle accelerator around the world. These simple devices provide non-invasive high level beam measurements, but…
QP Based Constrained Optimization for Reliable PINN Training
Alan Williams, Christopher Leon, Alexander Scheinker
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for integrating physics-based constraints and data to address forward and inverse problems in machine learn…