Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic

Abstract

Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 y se referencia usando el siguiente formato: L. Lara, J. Brito, C. Graciano, "Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic", UIS Ingenierías, vol. ABSTRACTThis paper presents a numerical assessment on the performance of two structural control strategies based on magnetorheological (MR) dampers. At first, a control strategy based on artificial neural networks was employed on a simple structure to control vibration. This controller combines a predictive model function to control forces and an inverse model of voltage calculation to manage the MR dampers. Secondly, a control strategy based on fuzzy logic was also used. Therefore, the controller governs the actions from a set of rules that represent the heuristics of the system to be controlled. Results achieved from the numerical simulations indicate that the performance of these two control strategies is promising and satisfactory, based on response reductions of up to 83% relative to the performance of the system without control. KEYWORDS:Control of structures, Vibration reduction, Magnetorheological dampers, Artificial neural networks, Fuzzy logic. RESUMENEn este trabajo se presenta una evaluación numérica sobre el desempeño de dos estrategias de control estructural basado en amortiguadores magnetoreológicos (MR). En primer lugar, se empleó una estrategia de control basada en redes neuronales artificiales en una estructura simple para el control de vibraciones. Este controlador combina una función de modelo predictivo para las fuerzas de control y un modelo inverso del cálculo de la tensión para manejar los amortiguadores MR. En segundo lugar, se utilizó una estrategia de control basada en lógica difusa. De esta forma, el controlador gobierna las acciones de un conjunto de reglas que representan la heurística del sistema a controlar. Los resultados de las simulaciones numéricas indican que el rendimiento de estas dos estrategias de control es prometedor y satisfactorio, basado en la reducción de la respuesta de hasta un 83% en relación con el rendimiento del sistema sin control.

Research developments in adaptive intelligent vibration control of smart civil structures

“…ANNs based controller effectively reduced the structure's response as compared to the convention LQR controller. Lara et al 27 presented FBNNs named Nonlinear Auto-Regressive models with eXogenous Inputs (NARX)-NNs and FLC based control of 2-storey building structures equipped with MR damper; both of these controllers have produced significant results in achieving the control objective. However, the NARX-NNs-based controller performed better than the FLC controller.…”

“…[24][25][26] For the development of control algorithms and simulating structural behavior, the numerical model of the system is developed. 14,27 For their work, some control algorithms utilize numerical models of the system referred to as Model-Based Control (MBC)/ parametric control schemes, and some do not name as non-model-based/non-parametric control schemes. 21,28 Model-Based Control schemes should initially acquire a precise mathematical model for an existing system and design the controller afterward.…”

“…40,41 Additionally, these linear and nonlinear MBC schemes often require high computational exertion toward identifying and modeling systems instead of designing controllers. 27,42 Accordingly, to mitigate the mentioned shortcomings and make control schemes less reliant on the numerical system's model, the researchers integrate adaptive control with intelligent systems (control). 43 The above-discussed integration is performed in two ways.…”

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

“…When compared to the traditional LQR controller, the ANNs-based controller effectively lowered the structural response. Lara et al [18] Presented two controllers; a feedback neural network named Nonlinear Auto-Regressive models with eXogenous Inputs (NARX-NNs) and FLC, to control a 2-story building structure equipped with MR damper. Their study evidenced that both these controllers had generated substantial results in reaching the control aim.…”

Adaptive feedback feedforward compensation for disturbance rejection in a one DOF flexible structure: comparative analysis

“…Least Square and regression models commonly used in Active Noise Control (ANC) to model without using conventional simplifying assumption regarding the physical plant to be controlled are the Filtered-X LMS (FxLMS) and RLS due to its simplicity in calculation and Digital Signal Processing (DSP) implementation to adaptive filtering in contrast to the result in system identification [6][7][8][9][10][11].…”