Fuzzy Logic application in buildings vibration control in civil engineering

摘要

The aim of the current research was investigating the application of fuzzy logic when controlling building vibrations. Thus, this research has introduced fuzzy and neuro-fuzzy system and has discussed the role of neuro-fuzzy system when helping to improve the response to the issue of controlling building vibrations. According to the results, using neuro-fuzzy inference system could regulate membership function parameters and fuzzy controller could control and reduce the damages to buildings up to 20%. The used system was a fuzzy controller of Sugeno type having 2 inputs, 1 output and 9 rules. After applying the data on the system, it faced the sum of square errors equal to 0.5150. KEWORDS: Fuzzy logic, Building vibrations, Neuro-fuzzy inference system Introduction Controlling buildings vibrationcaused by wind or earthquake can be performed by passive, active, semi-active or compound control systems. In each of the above control systems, different tools are provided to reduce the seismic responses. Since the semi-active systems are reliable and tolerable like passive and active systems, it has attracted a number of researcher's attention in the field of structural control. Extensive studies have shown that semi-active systems have the ability to access the performance of an active system to a large extent which significantly operates better than that of passive systems. Although in the structural engineering, the reduction of damage caused by large loads is the most important purpose, but, since now, there has not been enough attention to ensure that damage indicators are directly controlled. This is because valid damage indicators include variety, while modern control theories includeing LQR , LQG, and sliding mode control, which are based on space-state model, can only incorporate state variables in performance indicators. Due to easy implementation, Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) are used as theoretical basic methods of modern control, the most common linear optimization control theory in engineering issues, and the most common algorithm in the calculation of control forces which can be used in active and semi-active structural control [2]. Fuzzy inference system is a method that can be used as a classical algorithm with far more extensive capabilities than Neural Networks and Evolutionary Algorithms and its modeling can be performed by lingual variables which provide system modeling and less computational intelligence to the control system. Fuzzy inference system is a systematic process that converts a database into a nonlinear map. Hence, the systems based on knowledge (including fuzzy system) are used in engineering and decision-making applications In 1975, Mamdani and Asilian used the fuzzy inference system to control the ingredients of a steam engine and boiler using lingual control rules in experiences of human operators (Mamdani and Asilian, 1975). In 1978, Holmblad and Wooster used the first fuzzy controller to control a complete industrial process like cement kiln. Since then, the fuzzy controllers were used in many industrial processes and devices such as subways and robotics and many other issues that need decision-making. In 2007, Pourzeynali et al., studied the active structural control of high structures using fuzzy logic. Kim and Kang (2012), introduced the semi-active control of a structure using a multi-purpose Genetic Algorithm. In 2004, Aldawod et al., used fuzzy control to control the active structural nonlinear behavior in terms of seismic stimulation. In 2006, Rilay and Simeanz, studied the viscous semi-active damper and fuzzy control on the separated structure. Also in 2006, Kim et al., used fuzzy control and magnetorheological damper (MR) to control the separated structure. In 2003, Samali et al., studied the experimental model of five-floor structure as well as Active Massive Damper (ATMD) in terms of different earthquakes using Linear Quadratic Regulator (LQR) a