1 citations · 1 across the 13 of their papers we have counts for
13 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 Krstić +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…
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…
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…
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…
Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space
Mahindra Rautela, Alan Williams, Alexander Scheinker
Complex dynamical systems, such as particle accelerators, require tuning over high-dimensional, nonlinear state and parameter spaces while experimental measurements and high-fideli…
Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
Mahindra Rautela, Alan Williams, Alexander Scheinker
Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behav…
Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams
Mahindra Rautela, Alan Williams, Alexander Scheinker
Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (…
Accelerator system parameter estimation using variational autoencoded latent regression
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustm…
Practical Safe Extremum Seeking with Assignable Rate of Attractivity to the Safe Set
Alan Williams, Miroslav Krstic, Alexander Scheinker
We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured objective function while maintaining a measured metric of safety (a control barrie…
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators
Mahindra Rautela, Alan Williams, Alexander Scheinker
Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to lim…
Semi-Global Practical Extremum Seeking with Practical Safety
Alan Williams, Miroslav Krstic, Alexander Scheinker
We introduce a type of safe extremum seeking (ES) controller, which minimizes an unknown objective function while also maintaining practical positivity of an unknown barrier functi…