Merge pull request #87 from StanfordASL/update_bib

Update bib

_bibliography/ASL_Bib.bib

@@ -1568,6 +1568,7 @@ @InProceedings{SinghalGammelliEtAl2024
   month     = jun,
   note      = {In press},
   abstract  = {Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle vehicles to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the E-AMoD control problem through the lens of reinforcement learning and propose a graph network-based framework to achieve drastically improved scalability and superior performance over heuristics. Specifically, we adopt a bi-level formulation where we (1) leverage a graph network-based RL agent to specify a desired next state in the space-charge graph, and (2) solve more tractable linear programs to best achieve the desired state while ensuring feasibility. Experiments using real-world data from San Francisco and New York City show that our approach achieves up to 89% of the profits of the theoretically-optimal solution while achieving more than a 100x speedup in computational time. We further highlight promising zero-shot transfer capabilities of our learned policy on tasks such as inter-city generalization and service area expansion, thus showing the utility, scalability, and flexibility of our framework. Finally, our approach outperforms the best domain-specific heuristics with comparable runtimes, with an increase in profits by up to 3.2x.},
+  doi       = {10.23919/ecc64448.2024.10591098},
   keywords  = {press},
   owner     = {gammelli},
   timestamp = {2023-11-15},
@@ -2179,7 +2180,7 @@ @InProceedings{RibeiroLukeEtAl2023
   address   = {Doha, Qatar},
   month     = dec,
   abstract  = {Although vehicle electrification and utilization of on-site solar PV generation can begin reducing the greenhouse gas emissions associated with bus fleet operations, a method to intelligently coordinate bus-route assignments, bus charging, and energy storage is needed to fully decarbonize fleet operations while simultaneously minimizing electricity costs. This paper proposes a 24/7 Carbon-Free Electrified Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar PV arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework, and for a case study of Stanford University's Marguerite Shuttle, the platform reduced peak charging demand by 99%, electric utility bill by $2778, and associated carbon emissions by 100% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by $784 and emissions by 63%.},
-  doi       = {https://doi.org/10.46855/energy-proceedings-11033},
+  doi       = {10.46855/energy-proceedings-11033},
   owner     = {jthluke},
   timestamp = {2023-11-15},
   url       = {https://www.energy-proceedings.org/towards-a-24-7-carbon-free-electric-fleet%3A-a-digital-twin-framework/},
@@ -2601,6 +2602,18 @@ @inproceedings{PaparussoKousikEtAl2024
   url       = {/wp-content/papercite-data/pdf/Paparusso.ea.ICRA24.pdf}
 }
 
+@inproceedings{PabonEtAl2024,
+  author    = {Pabon, L. and Köhler, J. and Alora, J.I. and Eberhard, P.B. and Carron, A. and Zeilinger, M.N. and Pavone, M.},
+  title     = {Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer},
+  year      = {2024},
+  booktitle = proc_IEEE_IROS,
+  address   = {Abu Dhabi},
+  abstract  = {In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and compensates for periodic disturbances. We present the design of the observer and the accompanying tracking MPC scheme, proving that their combination achieves zero tracking error asymptotically, regardless of the complexity of the unmodelled dynamics. We validate the effectiveness of our method, demonstrating asymptotically perfect tracking on a high-dimensional soft robot with nearly 10,000 states and a fivefold reduction in tracking errors compared to a baseline MPC on small-scale autonomous race car experiments.},
+  url       = {https://arxiv.org/abs/2404.01550},
+  owner     = {lpabon},
+  timestamp = {2024-07-01}
+}
+
 @article{OnoPavoneEtAl2015,
   author    = {Ono, M. and Pavone, M. and Kuwata, Y. and Balaram, J.},
   title     = {Chance-Constrained Dynamic Programming with Application to Risk-Aware Robotic Space Exploration},
@@ -5136,18 +5149,6 @@ @inproceedings{ArenaFortunaEtAl2005
   url       = {/wp-content/papercite-data/pdf/Arena.Fortuna.ea.ISCAS05.pdf}
 }
 
-@inproceedings{PabonEtAl2024,
-  author    = {Pabon, L. and Köhler, J. and Alora, J.I. and Eberhard, P.B. and Carron, A. and Zeilinger, M.N. and Pavone, M.},
-  title     = {Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer},
-  year      = {2024},
-  booktitle = proc_IEEE_IROS,
-  address   = {Abu Dhabi},
-  abstract  = {In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and compensates for periodic disturbances. We present the design of the observer and the accompanying tracking MPC scheme, proving that their combination achieves zero tracking error asymptotically, regardless of the complexity of the unmodelled dynamics. We validate the effectiveness of our method, demonstrating asymptotically perfect tracking on a high-dimensional soft robot with nearly 10,000 states and a fivefold reduction in tracking errors compared to a baseline MPC on small-scale autonomous race car experiments.},
-  url       = {https://arxiv.org/abs/2404.01550},
-  owner     = {lpabon},
-  timestamp = {2024-07-01}
-}
-
 @inproceedings{AloraPabonEtAl2023,
   author    = {Alora, J.I. and Pabon, L. and Köhler, J., and Cenedese, M. and Schmerling, E. and Zeilinger M. N. and Haller, G. and Pavone, M.},
   title     = {Robust Nonlinear Reduced-Order Model Predictive Control},