Search for: All records

Trajectory planning plays a crucial role in autonomous driving and navigation by enabling robots to generate safe paths while minimizing travel costs and avoiding collisions. This paper addresses the issue of predicting dynamic obstacles for safe trajectory planning when prior information is unavailable and detection range is limited. We propose a learning framework using Gaussian Processes (GP) for motion prediction and uncertainty estimation, further enhanced by Recurrent Neural Networks (RNN) for more accurate predictions. In addition, we develop a receding horizon planning method, formulated as a stochastic optimization problem, to ensure safe, collision-free paths with confidence probabilities. Together, these contributions provide a robust framework for adaptive and safe trajectory generation in dynamic environments. Simulations were performed to demonstrate the effectiveness of the proposed strategy, where our approach (combining GP and RNN) outperformed a baseline method that utilized only GP. 

Code for preprint on arXiv in 2026/06: "Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification" 

This paper presents a communication-efficient framework for distributed optimization using the Alternating Direction Method of Multipliers (ADMM). Building on prior work integrating Gaussian process regression and uniform quantization, we propose novel quantization strategies that adapt both mid-value, window length, and resolution at each iteration. We introduce joint and individual mechanisms for making communication decisions and assigning quantization bits based on agent-specific uncertainties. To address performance degradation near convergence, where variance stagnates, we incorporate a hybrid approach that switches to a quantization refinement method when prediction improvements plateau. This hybrid scheme maintains algorithmic convergence while significantly reducing communication overhead. Extensive simulations on a distributed quadratic cost-sharing problem demonstrate that the proposed methods notably decrease the number of transmitted bits while preserving solution accuracy. Among all tested approaches, the hybrid method offers the most robust and consistent trade-off between accuracy and communication efficiency across a wide range of parameter settings. 

This data set contains measurements from real HVAC (heating, ventilation, and air conditioning) systems of real buildings in the US.  Each ZIP file contains CSV data files of a building for different scenarios.  Refer to the README file in each ZIP file for details. The document `data_info.pdf` provides explanations of the variables/columns in the data files. This work was supported by the U.S. National Science Foundation (NSF) under grants 2514584 and 2513096. 

Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generalize well. In this paper, we propose a novel method for data-driven system identification by integrating a neural network as the first-order derivative of the learned dynamics in a Taylor series instead of learning the dynamical function directly. In addition, for dynamical systems with known monotonic properties, our approach can ensure monotonicity by constraining the neural network derivative to be non-positive or non-negative to the respective inputs, resulting in Monotonic Taylor Neural Networks (MTNN). Such constraints are enforced by either a specialized neural network architecture or regularization in the loss function for training. The proposed method demonstrates better performance compared to methods without the physics-based monotonicity constraints when tested on experimental data from an HVAC system and a temperature control testbed. Furthermore, MTNN shows good performance in the control application of a model predictive controller for a practical nonlinear MIMO system. 

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, such as structural priors, Lyapunov functions, Control Barrier Functions, predictive control, projections, and robust optimization techniques, ensuring that the learned models respect stability and safety criteria. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.