Mr. Khalid M. Abdelaziz Profile

Khalid M. Abdelaziz

at Kansas State Univ

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Proceedings Article | 18 April 2022 Presentation + Paper

Adaptive flow-induced vibration control using distributed sensor/actuator networks with gated recurrent units and genetic algorithms

Khalid Abdelaziz, Jared Hobeck

Proceedings Volume 12046, 120460G (2022) https://doi.org/10.1117/12.2630805

KEYWORDS: Buildings, Sensors, Genetic algorithms, Sensor networks, Control systems, Computer programming, Wind measurement, Genetics, Vibration control

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This work presents a control scheme for wind-induced vibration mitigation for tall buildings based on a gated recurrent unit (GRU) encoder-decoder model which operates using readings from multiple sensors to define a unique system state. The sensors include a distributed network of pressure probes installed on surrounding buildings, accelerometers installed on the principal building, and atmospheric conditions. The encoder-decoder GRU is trained from timeseries sensor readings to construct a unique internal representation (hidden state) of the evolving wind and building conditions. A 1:400-scale aeroelastic building model with motorized plates acting as aerodynamic control surfaces is used in wind tunnel experiments to conduct this study. An online genetic reinforcement learning (GRL) algorithm uses a series of multilayer perceptron (MLP) networks to determine optimum actuator orientations for different flow conditions. The algorithm stores previously discovered solutions in the MLPs sorted by their fitness. The GA operates by obtaining a solution from each of the MLPs and performing GA operations on them to choose the next combination of plate angles to try. A chance also exists for trying completely random plate angles to prevent the GA from stalling. The MLPs are continuously trained during online optimization using findings obtained from new trials. The system eliminates the need for holding wind conditions, which are uncontrollable, constant during online training but still uses a pseudo-random search technique to obtain global optimum solutions. Results show a considerable reduction in building RMS acceleration when compared with a large collection of results with random constant plate angle orientations.

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