We present a physics-constrained deep learning method to develop control-oriented models
of building thermal dynamics. The proposed method uses systematic encoding of physics …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …

We present a novel approach to modelling and learning vector fields from physical systems
using neural networks that explicitly satisfy known linear operator constraints. To achieve …

We present an alternative to model predictive control (MPC) for unknown nonlinear systems
in low-resource embedded device settings. The structure of the presented datadriven control …

When deploying autonomous agents in unstructured environments over sustained periods
of time, adaptability and robustness oftentimes outweigh optimality as a primary …

The PAC-Bayesian framework has significantly advanced our understanding of statistical
learning, particularly in majority voting methods. However, its application to multi-view …

We introduce a new control paradigm termed Phasor-Based Control (PBC) to coordinate
Distributed Energy Resources (DER) for improved voltage regulation and other objectives …

Symmetry group is an important construct to understand the behaviour of a pure
mathematical or a physical system including system of differential equations. We develop a …

Design and Control of an Active Ankle-Foot Orthosis Page 1 Design and Control of an Active
Ankle-Foot Orthosis by Benjamin DeBoer A thesis submitted to the School of Graduate and …

Federated learning (FL) has emerged as a promising paradigm for training decentralized
machine learning models with privacy preservation. However, FL models are biased, which …