Neural network predictive control of vibrations in tall structure: An experimental controlled vision. (January 2021) - Jamil, Mohsin; Khan, Muhammad Nasir; Rind, Saqib Jamshed; Awais, Qasim; Uzair, Muhammad
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. Graphical abstract: Highlights: A neural network model predictive controller for vibration control of tall structures excited by wind and earthquake. Implementation of neural network MPC on a scaled-down laboratory model of a tall structure. Implementation of neural network MPC over an active tuned mass damper.