Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
Learning reference governor for constrained spacecraft rendezvous and proximity maneuvering
Spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) play
significant roles in many space missions, including on-orbit servicing and active debris …
significant roles in many space missions, including on-orbit servicing and active debris …
Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in spark-ignition engines at high exhaust gas recirculation–diluted conditions
Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while
maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the …
maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the …
Prescribed time recovery from state constraint violation via approximation-free control approach
Y Cao, Z Shen, J Cao, D Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
For systems with soft state constraints, initial violation in such constraints is acceptable if no
feasible control strategy capable of maintaining such constraints exists or an excessively …
feasible control strategy capable of maintaining such constraints exists or an excessively …
Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines
In recent years, as engine control strategies have grown increasingly sophisticated in a
continued drive for increasing efficiency and reducing emissions, engine operation has …
continued drive for increasing efficiency and reducing emissions, engine operation has …
Hierarchical model predictive control for optimization of vehicle speed and battery thermal using vehicle connectivity
X Piao, X Wang, K Han - IEEE Access, 2021 - ieeexplore.ieee.org
Extreme ambient temperatures cause electric vehicles' batteries to deteriorate and have a
major impact on driving range, which is a barrier for mass production of electric vehicles …
major impact on driving range, which is a barrier for mass production of electric vehicles …
Robust MPC-RG for an autonomous racing vehicle considering obstacles and the battery state of charge
The design of a controller able to deal with uncertainties and physical constraints plays an
essential role in fast and complex systems. Then, a reference governor approach based on …
essential role in fast and complex systems. Then, a reference governor approach based on …
Model-free learning for safety-critical control systems: A reference governor approach
This paper describes a learning-based approach to operating safety-critical control systems.
A reference governor is an add-on scheme used to guard the nominal system against …
A reference governor is an add-on scheme used to guard the nominal system against …
Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation
A Chen, K Mitsopoulos… - 2023 62nd IEEE …, 2023 - ieeexplore.ieee.org
Unmanned autonomous vehicles (UAVs) rely on effective path planning and tracking control
to accomplish complex tasks in various domains. Reinforcement Learning (RL) methods are …
to accomplish complex tasks in various domains. Reinforcement Learning (RL) methods are …
Improving autonomous vehicle in‐traffic safety using learning‐based action governor
Abstract The Action Governor (AG) is a supervisory scheme augmenting a nominal control
system in order to enhance the system's safety and performance. It acts as an action filter …
system in order to enhance the system's safety and performance. It acts as an action filter …