Adaptive Parameterized Control for Coordinated traffic Management Using Reinforcement Learning

Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...

基于强化学习补偿的地面无人战车行进间跟瞄自适应控制

To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main...

Intelligent Controller Selection for Aggressive Quadrotor Manoeuvring: A reinforcement learning approach

A novel intelligent controller selection method for quadrotor attitude and altitude control is presented that maintains performance in different regimes of the flight envelope. Conventional quadrotor controllers can behave insufficiently during aggressive manoeuvring, in extreme angles the quadrotor is unable to maintain height which may result...