Adaptive Group-based Signal Control by Reinforcement Learning

Reinforcement Learning For Adaptive Traffic Signal Control

By 2050, two-thirds of the world’s 9.6 billion people will live in urban areas [2]. In many cities, opportunities to expand urban road networks are limited, so existing roads will need to more efficiently accommodate higher volumes of traffic. Consequently, there is a pressing need for technologically viable, low-cost solutions that can work with existing infrastructure to help alleviate increa...

Reinforcement Learning Based PID Control of Wind Energy Conversion Systems

In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

Reinforcement learning in neurofuzzy traffic signal control

Adaptive fuzzy control of satellite attitude by reinforcement learning

The attitude control of a satellite is often characterized by a limit cycle, caused by measurement inaccuracies and noise in the sensor output. In order to reduce the limit cycle, a nonlinear fuzzy controller was applied. The controller was tuned by means of reinforcement learning without using any model of the sensors or the satellite. The reinforcement signal is computed as a fuzzy performanc...

Reinforcement Learning-based Quadcopter Control

Analysis of quadcopter dynamics and control is conducted. A linearized quadcopter system is controlled using modern techniques. A MATLAB quadcopter control toolbox is presented for rapid visualization of system response. Waypoint-based trajectory control of a quadcopter is performed and appended to the MATLAB toolbox. Finally, an investigation of control using reinforcement learning is conducted.