Yeesock Kim

Model-Based Multi-input, Multi-output Supervisory Semi-active Nonlinear Fuzzy Controller

: The authors recently proposed a new multi-input, single-output (MISO) semi-active fuzzy controller for vibration control of seismically excited small-scale buildings. In this article, the previously proposed MISO control system is advanced to a multi-input, multi-output (MIMO) control system through integration of a set of model-based fuzzy controllers that are formulated in terms of linear matrix inequalities (LMIs) such that the global asymptotical stability is guaranteed and the performance on transient responses is also satisfied. The set of model-based fuzzy controllers is divided into two groups: lower level controllers and a higher level coordinator. The lower level fuzzy controllers are designed using acceleration and drift responses; while velocity information is used for the higher level controller. To demonstrate the effectiveness of the proposed approach, an eight-story building structure employing magnetorheological (MR) dampers is studied. It is demonstrated from comparison of the uncontrolled and semi-active controlled responses that the proposed design framework is effective in vibration reduction of a building structure equipped with MR dampers.

Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied.

Wavelet-based AR–SVM for health monitoring of smart structures

This paper proposes a novel structural health monitoring framework for damage detection of smart structures. The framework is developed through the integration of the discrete wavelet transform, an autoregressive (AR) model, damage-sensitive features, and a support vector machine (SVM). The steps of the method are the following: (1) the wavelet-based AR (WAR) model estimates vibration signals obtained from both the undamaged and damaged smart structures under a variety of random signals; (2) a new damage-sensitive feature is formulated in terms of the AR parameters estimated from the structural velocity responses; and then (3) the SVM is applied to each group of damaged and undamaged data sets in order to optimally separate them into either damaged or healthy groups. To demonstrate the effectiveness of the proposed structural health monitoring framework, a three-story smart building equipped with a magnetorheological (MR) damper under artificial earthquake signals is studied. It is shown from the simulation that the proposed health monitoring scheme is effective in detecting damage of the smart structures in an efficient way.

System identification of smart structures using a wavelet neuro-fuzzy model

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure–MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input–output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure–MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Control of a Seismically Excited Benchmark Building Using Linear Matrix Inequality-Based Semiactive Nonlinear Fuzzy Control

This paper investigates the behavior of a seismically excited benchmark building employing magnetorheological dampers operated by a model-based fuzzy logic controller (MBFLC) formulated in terms of linear matrix inequalities (LMIs). The MBFLC is designed in a systematic way, while the traditional model-free fuzzy logic controller is designed via trial and error by experienced investigators. It is demonstrated from comparison of the uncontrolled and semiactive controlled responses that the proposed LMI-based MBFLC is effective in vibration reduction of a benchmark building under various earthquake loading conditions.

Sensor fault isolation and detection of smart structures

This paper proposes a novel principal component analysis (PCA)-based sensor fault isolation and detection method for smart structures: detectability and isolability of each sensor fault are analyzed using a PCA-based numeric residual generator and the probability of errors in each sensor is also determined using the Bayesian probabilistic analysis of these residuals. To demonstrate the performance of the proposed PCA-based sensor fault isolation and detection methodology, a seismically excited three-story building structure equipped with a magnetorheological damper that is operated by a semi-active nonlinear fuzzy control system is investigated. It is shown that the proposed PCA-based sensor fault diagnosis approach is effective in identifying sensor faults of smart structures for hazard mitigation of large structures as a model-free methodology.

Multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low- and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

Nonlinear system identification of smart structures under high impact loads

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure–MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure–MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.

Nonlinear multiclass support vector machine–based health monitoring system for buildings employing magnetorheological dampers

In this article, a nonlinear multiclass support vector machine–based structural health monitoring system for smart structures is proposed. It is developed through the integration of a nonlinear multiclass support vector machine, discrete wavelet transforms, autoregressive models, and damage-sensitive features. The discrete wavelet transform is first applied to signals obtained from both healthy and damaged smart structures under random excitations, and it generates wavelet-filtered signal. It not only compresses lengthy data but also filters noise from the original data. Based on the wavelet-filtered signals, several wavelet-based autoregressive models are then constructed. Next, damage-sensitive features are extracted from the wavelet-based autoregressive coefficients and then the nonlinear multiclass support vector machine is trained by a variety of damage levels of wavelet-based autoregressive coefficient sets in an optimal method. The trained nonlinear multiclass support vector machine takes new test wavelet-based autoregressive coefficients that are not used in the training process and quantitatively estimates the damage levels. To demonstrate the effectiveness of the proposed nonlinear multiclass support vector machine, a three-story smart building equipped with a magnetorheological damper is studied. As a baseline, naive Bayes classifier–based structural health monitoring system is presented. It is shown from the simulation that the proposed nonlinear multiclass support vector machine–based approach is efficient and precise in quantitatively estimating damage statuses of the smart structures.

Wavelet-neuro-fuzzy control of hybrid building-active tuned mass damper system under seismic excitations

This paper proposes a wavelet-based fuzzy neuro control algorithm for the hazard mitigation of seismically excited buildings equipped with a hybrid control system. The wavelet-based fuzzy neuro controller is developed through the integration of discrete wavelet transform, artificial neural network, and a Takagi-Sugeno fuzzy controller. The hybrid control system is an integrated model of an actuator, a tuned mass damper, and viscous liquid dampers: an active tuned mass damper (ATMD) is located on the top floor of the structure and viscous liquid dampers are located on each floor. To demonstrate the effectiveness of the proposed wavelet-based adaptive neuro-fuzzy inference system (WANFIS) controller, an eight-story building employing passive viscous liquid dampers as well as an ATMD is investigated. A variety of earthquakes such as an artificial earthquake, the 1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes are used as disturbance signals. It is demonstrated that the WANFIS controller is effective in reducing the structural responses of the hybrid structure system subjected to a variety of disturbances.