A WANFIS Model for Use in System Identification and Structural Control of Civil Engineering Structures

With the increased deterioration of infrastructure in this country, it has become important to find ways to maintain the strength and integrity of a structure over its design life. Being able to control the amount a structure displaces or vibrates during a seismic event, as well as being able to model this nonlinear behavior, provides a new challenge for structural engineers. This research proposes a wavelet-based adaptive neuro- fuzzy inference system for use in system identification and structural control of civil engineering structures. This algorithm combines aspects of fuzzy logic theory, neural networks, and wavelet transforms to create a new system that effectively reduces the number of sensors needed in a structure to capture its seismic response and the amount of computation time needed to model its nonlinear behavior. The algorithm has been tested for structural control using a three-story building equipped with a magnetorheological damper for system identification, an eight-story building, and a benchmark highway bridge. Each of these examples has been tested using a variety of earthquakes, including the El-Centro, Kobe, Hachinohe, Northridge, and other seismic events

Characterization and modeling of a new magnetorheological damper with meandering type valve using neuro-fuzzy

This paper presents the characterization and hysteresis modeling of magnetorheological (MR) damper with meandering type valve. The meandering type MR valve, which employs the combination of multiple annular and radial flow passages, has been introduced as the new type of high performance MR valve with higher achievable pressure drop and controllable performance range than similar counterparts in its class. Since the performance of a damper is highly determined by the valve performance, the utilization of the meandering type MR valve in an MR damper could potentially improve the damper performance. The damping force characterization of the MR damper is conducted by measuring the damping force as a response to the variety of harmonic excitations. The hysteresis behavior of the damper is identified by plotting the damping force relationship to the excitation displacement and velocity. For the hysteresis modeling purpose, some parts of the data are taken as the training data source for the optimization parameters in the neuro-fuzzy model. The performance of the trained neuro-fuzzy model is assessed by validating the model output with the remaining measurement data and benchmarking the results with the output of the parametric hysteresis model. The validation results show that the neuro-fuzzy model is demonstrating good agreement with the measurement results indicated by the average relative error of only around 7%. The model also shows robustness with no tendency of growing error when the input values are changed

Neuro-fuzzy modeling of a sponge-type MR damper

Numerical modeling of MR dampers based on parametric models constitutes one of the main methodologies to simulate the behavior of this type of devices. However, its highly non-linear nature and also its inherent rheological behavior make this type of numerical modeling harsh and complicated, which hinders the development of simple models capable to cover all aspects associated with the proper numerical simulation of the damper behavior and therefore usually complex parametric models involving several parameters are required to achieve a reliable and accurate representation of its rheological behavior. Hence, non-parametric models represent another feasible approach to simulate the complex non-linear behavior of MR dampers although in this case allowing to obtain a wide-ranging numerical model without the need to define or identify a large number of model parameters. In this context, we attempt to model and predict the response of a sponge-type MR damper using a non-parametric modeling technique based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Initially, the basic structure of this data modeling technique is presented and the main aspects regarding the development of a neuro-fuzzy model for MR dampers are addressed. Then, an ANFIS modeling technique is developed to obtain a non-parametric model for the MR damper. Finally, a comparison between the numerical and experimental results will be presented to validate the selected modeling technique.info:eu-repo/semantics/publishedVersio

System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers

Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller

Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison

Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain

State of the art of control schemes for smart systems featuring magneto-rheological materials

This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

Hybrid fuzzy neural network: genetic algorithm applied to the control of magnetorheological and smart material vehicle semiactive suspensions

isolate the passenger and the structure from vibrations and ensure the vehicle stability under different travel condition. A semiactive suspension system is defined as set of mechanical and electric devices that includes semiactive or active elements whose parameters could be controlled by a system, although they cannot introduce forces into the system. An option of devices that can be used in semiactive suspension system could be elements which their mechanical properties can be controlled by an external stimulus, such capability is produced by the so-called “Smart Materials”. This work analyses the application of a kind of those materials which can modify their mechanical properties by the influence of a magnetic field, that category is called magnetorheologic materials. Those allow through the intensity of a magnetic field tune their behavior in terms of their viscoelastic properties. The control of this devices requires a sensing and actuator systems and the response between them requires a so small delay process, otherwise the suspension system does not achieve its objective, but also is considered unsafe. Therefore, a more efficient model using artificial intelligence techniques such Fuzzy Logic and Artificial Neural Network is developed to simulate and keep the system response updated through the time due to variations on the elements behavior such wear or another factors. Furthermore, the optimization of the response and the element design is complex because of the high non-linear system and the large number of degrees of freedom that the system have, therefore the application of Genetic Algorithm to improve the time of calculus and design process. This article presents a short introduction of smart materials focused on Magnetorheological materials such fluids and elastomer, also a modeling of suspension devices based on this technology are modeled. These elements are introduced in a vehicle model to evaluate the results of performances and compare with a passive suspension system