Simultaneous vibration control and energy harvesting using actor-critic based reinforcement learning

Mitigating excessive vibration of civil engineering structures using various types of devices has been a conspicuous research topic in the past few decades. Some devices, such as electromagnetic transducers, which have a capability of exerting control forces while simultaneously harvesting energy, have been proposed recently. These devices make possible a self-regenerative system that can semi-actively mitigate structural vibration without the need of external energy. Integrating mechanical, electrical components, and control algorithms, these devices open up a new research domain that needs to be addressed. In this study, the feasibility of using an actor-critic based reinforcement learning control algorithm for simultaneous vibration control and energy harvesting for a civil engineering structure is investigated. The actor-critic based reinforcement learning control algorithm is a real-time, model-free adaptive technique that can adjust the controller parameters based on observations and reward signals without knowing the system characteristics. It is suitable for the control of a partially known nonlinear system with uncertain parameters. The feasibility of implementing this algorithm on a building structure equipped with an electromagnetic damper will be investigated in this study. Issues related to the modelling of learning algorithm, initialization and convergence will be presented and discussed.

[1]  Yuxi Li,et al.  Deep Reinforcement Learning: An Overview , 2017, ArXiv.

[2]  Jeffrey T. Scruggs,et al.  Multi-objective optimal control of vibratory energy harvesting systems , 2012 .

[3]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[4]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[5]  Lei Zuo,et al.  Large-scale vibration energy harvesting , 2013 .

[6]  W. D. IWAN.  STRUCTURAL CONTROL USING REGENERATIVE FORCE ACTUATION NETWORKS , 2002 .

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Billie F. Spencer,et al.  Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction , 1996 .

[10]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[11]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[12]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[13]  Chih-Chen Chang,et al.  Self-powered hybrid electromagnetic damper for cable vibration mitigation , 2017 .

[14]  Guy Lever,et al.  Deterministic Policy Gradient Algorithms , 2014, ICML.

[15]  Lei Zuo,et al.  Dual-Functional Energy-Harvesting and Vibration Control: Electromagnetic Resonant Shunt Series Tuned Mass Dampers. , 2013 .

[16]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[17]  Wenai Shen,et al.  Harvesting energy via electromagnetic damper: Application to bridge stay cables , 2015 .

[18]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[19]  B. F. Spencer,et al.  STATE OF THE ART OF STRUCTURAL CONTROL , 2003 .

[20]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  D. Guyomar,et al.  Buck-Boost Converter for Sensorless Power Optimization of Piezoelectric Energy Harvester , 2007, IEEE Transactions on Power Electronics.