10 citations · 21 across the 6 of their papers we have counts for
6 papers
Koopman-Based Robust Model Predictive Control for Nonlinear Systems with Stochastic Intermittent Measurements
Guanhua Liu, Tong Wu, Lixian Zhang +2
Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonlinear systems because prediction uncertainty grows during feedback outage…
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
Yuan Tian, Minghao Han, Chetan Kulkarni +1
Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing th…
Modeling and Control of an Omnidirectional Micro Aerial Vehicle Equipped with a Soft Robotic Arm
Róbert Szász, Mike Allenspach, Minghao Han +2
Flying manipulators are aerial drones with attached rigid-bodied robotic arms and belong to the latest and most actively developed research areas in robotics. The rigid nature of t…
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data e…
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Minghao Han, Lixian Zhang, Jun Wang +1
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigatio…
Model-free Reinforcement Learning with Robust Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in…