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Found 4 papers, 0 papers with code
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.
Automated Controller Calibration by Kalman Filtering
The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system.
