Application of reinforcement learning for active noise control
نویسندگان
چکیده
منابع مشابه
Self-learning active noise control.
An important step for active noise control (ANC) systems to be practical is to develop model independent ANC (MIANC) systems that tolerate parameter variations in sound fields. Reliabilities and stabilities of many MIANC systems depend on results of online system identifications. Parameter errors due to system identifications may threaten closed-loop stabilities of MIANC systems. A self-learnin...
متن کاملReinforcement Learning for Active Length Control of Shape Memory Alloys
The ability to actively control the shape of aerospace structures has initiated research regarding the use of Shape Memory Alloy actuators. These actuators can be used for morphing or shape change by controlling their temperature, which is effectively done by applying a voltage difference across their length. The ability to characterize this temperature-strain relationship using Reinforcement L...
متن کاملNetwork-based Learning of Active Noise Control
1 Zbigniew Ogonowski, Silesian University of Technology, 44-100 Gliwice, Akademicka 16, Poland, [email protected] Abstract The paper presents a concept of network-based learning of Active Noise Control (ANC) problems. Arguments motivating remote experimenting are given. It follows from the presentation that basic experiments can be done automatically using “remote design”. The mo...
متن کاملReinforcement 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...
متن کاملReinforcement Learning for Control
Reinforcement learning (RL) offers a principled way to control nonlinear stochastic systems with partly or even fully unknown dynamics. Recent advances in areas such as deep learning and adaptive dynamic programming (ADP) have led to significant inroads in applications from robotics, automotive systems, smart grids, game playing, traffic control, etc. This open track provides a forum of interac...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
سال: 2017
ISSN: 1300-0632,1303-6203
DOI: 10.3906/elk-1602-189