Yasin Sonmez

Neural network (NN) controllers achieve strong empirical performance on nonlinear dynamical systems, yet deploying them in safety-critical settings requires robustness to disturbances and uncertainty. We present a method for jointly synthesizing NN controllers and dissipativity certificates that formally guarantee robust closed-loop performance using adversarial training, in which we use counterexamples to the robust dissipativity condition to guide training. Verification is done post-training using alpha,beta-CROWN, a branch-and-bound-based method that enables direct analysis of the nonlinear dynamical system. The proposed method uses quadratic constraints (QCs) only for characterization of non-parametric uncertainties. The method is tested in numerical experiments on maximizing the volume of the set on which a system is certified to be robustly dissipative. Our method certifies regions up to 78 times larger than the region certified by a linear matrix inequality-based approach that we derive for comparison.

Imitation learning is a promising approach for training autonomous vehicles (AV) to navigate complex traffic environments by mimicking expert driver behaviors. However, a major challenge in this paradigm lies in effectively utilizing available driving data, as collecting new data is resource-intensive and often limited in its ability to cover diverse driving scenarios. While existing imitation learning frameworks focus on leveraging expert demonstrations, they often overlook the potential of additional complex driving data from surrounding traffic participants. In this paper, we propose a data augmentation strategy that enhances imitation learning by leveraging the observed trajectories of nearby vehicles, captured through the AV’s sensors, as additional expert demonstrations. We introduce a vehicle selection sampling strategy that prioritizes informative and diverse driving behaviors, contributing to a richer and more diverse dataset for training. We evaluate our approach using the state-of-the-art learning-based planning method PLUTO on the nuPlan dataset and demonstrate that our augmentation method leads to improved performance in complex driving scenarios. Specifically, our method reduces collision rates and improves safety metrics compared to the baseline. Notably, even when using only 10% of the original dataset, our method achieves performance comparable to that of the full dataset, with improved collision rates. Our findings highlight the importance of leveraging diverse real-world trajectory data in imitation learning and provide insights into data augmentation strategies for autonomous driving.

Safety-critical control of autonomous systems requires formal safety certificates, such as controlled invariant sets, that must be computed online as conditions change. Although standard synthesis algorithms scale poorly with state dimension, monotone dynamical systems with lower-closed safety specifications allow for accelerated computation of controlled invariant sets. We introduce a threshold-function reformulation in which a lower-closed set on a d-dimensional grid is represented by its column heights along a designated axis. This yields an embarrassingly parallel iteration with asymptotically lower computational complexity than the lazy fixed-point algorithm. Experiments synthesize invariant sets on 3D grids with 10^9 cells in under 50 ms and 10^14 cells in under two minutes.

Tight coupling between computation, communication, and control pervades the design and application of cyber-physical systems (CPSs). Due to the complexity of these systems advanced design procedures that account for these tight interconnections are paramount to ensure the safe and reliable operation of control algorithms under computational constraints. This paper presents the Simulator for Hardware Architecture and Real-time Control (SHARC) to assist in the co-design of control algorithms and the computational hardware on which they are run. SHARC simulates the execution of a user-specified control algorithm on a given processor microarchitecture configuration, evaluating how computational constraints affect the dynamical properties of the closed-loop system. We illustrate the power of SHARC by examples of MPC applied to adaptive cruise control and the stabilization of an inverted pendulum. SHARC can be found at github.com/pwintz/sharc.

Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy. A commonly used simplifying assumption is that the dynamics and reward both exhibit the same symmetry; however, in many real-world environments, the dynamical model exhibits symmetry independent of the reward model. In this letter, we assume only the dynamics exhibit symmetry, extending the scope of problems in reinforcement learning and learning in control theory to which symmetry techniques can be applied. We use Cartan’s moving frame method to introduce a technique for learning dynamics that, by construction, exhibit specified symmetries. Numerical experiments demonstrate that the proposed method learns a more accurate dynamical model.

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space.

We propose an optimization method to place electric vehicle charging stations to minimize total travel time, thereby minimizing additional congestion and detours caused by the chargers. For a tractable optimization scheme, we frame the drivers’ route choices as a congestion game that allows us to find equilibrium flows for each candidate set of locations. Our contribution has two primary components. First, we refine the modeling of driver cost functions to account for charging needs as well as travel time, and introduce different agent types based on their unique valuations of charging benefits. Second, we address the exponential growth of the search space of charger locations with a greedy optimization approach. We demonstrate with numerical experiments that: (i) the congestion game formulation allows us to efficiently compute equilibrium flows for each candidate charger placement; (ii) the greedy approach can closely approximate the optimal selection.