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Found 8 papers, 0 papers with code
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
In this paper, we propose a transfer learning framework that adapts a control policy for excessive disturbance rejection of an underwater robot under dynamics model mismatch.
Machine Learning Accelerated PDE Backstepping Observers
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers.
Safety-Critical Traffic Control by Connected Automated Vehicles
Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability.
Robust Safety for Mixed-Autonomy Traffic with Delays and Disturbances
Considering both actuator and sensor delays, and disturbances, we propose robust safety-critical traffic control (RSTC) design to ensure ``robust safety'' of the mixed traffic.
Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road
The macroscopic traffic flow model is widely used for traffic control and management.
Neural Operators for Boundary Stabilization of Stop-and-go Traffic
To validate the efficacy of the proposed approach, simulations are conducted to compare the performance of the two neural operator controllers with a PDE backstepping controller and a Proportional Integral (PI) controller.
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
To address this concern, recent credit scoring research has considered a range of fairness-enhancing techniques put forward by the machine learning community to reduce bias and unfair treatment in classification systems.
Event-triggered Boundary Control of Mixed-autonomy Traffic
This paper is focused on suppressing traffic oscillations of the mixed-autonomy traffic system using boundary control design.
