Comparative analysis of semi-active control algorithms applied to magnetorheological dampers

Abstract

This paper analyzes the performance of three different semi-active control algorithms used to calculate and manage the optimal damping forces generated by a pair of magnetorheological (MR) dampers installed in a two-story building. The semi-active algorithms used are the linear quadratic regulator (LQR) associated with the clipped optimal algorithm, an algorithm based on a prediction model and a dynamic inverse model using nonlinear autoregressive exogenous (NARX)-type artificial neural networks, and a decision-making algorithm based on fuzzy logic. Additionally, the performances of the proposed semi-active algorithms are compared to those of passively used control devices. The results show response value reductions exceeding 50% for all the semi-active control strategies, which considerably outperformed the passive control. The three control strategies are thus confirmed as interesting tools with potential applications as MR damper administrators.Keywords: Structure dynamics, semi-active control of structures, vibration reduction, magnetorheological dampers, control algorithms, intelligent systems. RESUMEN En este artículo se analiza numéricamente la eficiencia y el desempeño de tres diferentes algoritmos de control semiactivo que tienen la función de calcular y administrar las fuerzas óptimas de amortiguamiento que deben ser generadas por un par de amortiguadores magnetoreológicos instalados en una estructura de dos niveles. Los algoritmos semi-activos empleados son el regulador lineal cuadrático (LQR), asociado al algoritmo clipped optimal y dos algoritmos fundamentados en sistemas inteligentes: un algoritmo basado en un modelo de predicción y en un modelo inverso dinámico construidos por medio de redes neuronales artificiales de tipo NARX y un algoritmo de toma de decisiones basados en lógica difusa. Adicionalmente, la actuación de los algoritmos de control semi-activo propuestos es comparada con el desempeño de los

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

“…A schematic of the neural networks applied to the force prediction model and the inverse model for the determination of the voltage is presented in Figure 2. Details of the definition, setup, training and validation of the NARX networks used for both the prediction model and the inverse model can be found in [30,35]. …”

“…This includes the treatment of structural control systems that use MR dampers. Therefore, research works focused on the control of structures dealt with the management of systems through various control algorithms based on mathematical models, fuzzy logic, genetic algorithms and neural networks [4,9,[21][22][23][24][25][26][27][28][29][30].…”