Neural network predictive control of vibrations in tall structure: An experimental controlled vision

M Jamil, MN Khan, SJ Rind, Q Awais, M Uzair - Computers & Electrical …, 2021 - Elsevier
Computers & Electrical Engineering, 2021Elsevier
This article presents the use of neural network predictive controller as a novel technique for
vibration control of tall structures employing single degree of freedom active tuned mass
damper (ATMD). Additionally, the proposed technique is compared with two modern control
techniques: pole-placement controller and adaptive neuro-fuzzy inference controller. A
scaled-down laboratory model is used to validate the control techniques. A linear and a
nonlinear auto-regressive exogenous (ARX) models are identified for the constructed …
Abstract
This article presents the use of neural network predictive controller as a novel technique for vibration control of tall structures employing single degree of freedom active tuned mass damper (ATMD). Additionally, the proposed technique is compared with two modern control techniques: pole-placement controller and adaptive neuro-fuzzy inference controller. A scaled-down laboratory model is used to validate the control techniques. A linear and a nonlinear auto-regressive exogenous (ARX) models are identified for the constructed structure. A neural network predictive controller is designed using the nonlinear ARX model. Polynomial and state-space pole-placement controllers are designed using the linear ARX model. A fuzzy logic controller is designed for the structure and trained using adaptive neuro fuzzy inference system (ANFIS). Hardware-in-the-loop implementation of these controllers demonstrates that the neural network predictive controller combines the advantages of both pole-placement and the ANFIS controllers and reduces the settling time of the mass damper six times with same amplitude mitigation.
Elsevier
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