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This paper focuses on characterization testing and validation of the dynamic model of a new type of high-damping density electromagnetic-based damping device (EMD) referred to as Magneto-Solid Damper (MSD) in which an eddy current damping mechanism acts in parallel with a solid friction mechanism to dissipate the input kinetic energy. The proposed MSD consists of a ferromagnetic plate, two copper plates placed on two sides of the ferromagnetic plate in parallel, and a planar array of permanent magnets (PMs) placed between the ferromagnetic plate and each of the two copper plates. The PMs arrays are attached to the ferromagnetic plate through non-magnetic friction pads. The friction force is developed between these friction pads and the ferromagnetic plate when the PMs arrays move relative to the ferromagnetic plate. The motion of the PMs arrays relative to the copper plates, on the other hand, generates the eddy current damping. A laboratory prototype of the proposed MSD has been designed and fabricated for the purpose of characterization testing. Finite element and simplified analytical models of this damper have also been developed to analyze the magnetic interaction between the PMs arrays and the ferromagnetic and copper plates. A dynamic model has been developed to characterize the force–displacement behavior of the proposed MSD. The parameters of this dynamic model are identified through a series of characterization tests on the prototype MSD under harmonic excitations of different frequencies. The identified dynamic model is validated by subjecting the prototype MSD to real ground motion records. It is concluded that that the proposed dynamic model is capable of describing the force–displacement behavior of the proposed MSD for a wide range of operating conditions.

Hydraulic actuators play a key role in experimental structural dynamics. In a previous study, a physics-based model for a servo-hydraulic actuator coupled with a nonlinear physical system was developed. Later, this dynamical model was transformed into controllable canonical form for position tracking control purposes. For this study, a nonlinear device is designed and fabricated to exhibit various nonlinear force-displacement profiles depending on the initial condition and the type of materials used as replaceable coupons. Using this nonlinear system, the controllable canonical dynamical model is experimentally validated for a servo-hydraulic actuator coupled with a nonlinear physical system.

Hydraulic actuators have been widely used to experimentally examine structural behavior at multiple scales. Real-time hybrid simulation (RTHS) is one innovative testing method that largely relies on such servo-hydraulic actuators. In RTHS, interface conditions must be enforced in real time, and controllers are often used to achieve tracking of the desired displacements. Thus, neglecting the dynamics of hydraulic transfer system may result either in system instability or sub-optimal performance. Herein, we propose a nonlinear dynamical model for a servo-hydraulic actuator (a.k.a. hydraulic transfer system) coupled with a nonlinear physical specimen. The nonlinear dynamical model is transformed into controllable canonical form for further tracking control design purposes. Through a number of experiments, the controllable canonical model is validated.

Real-time hybrid simulation (RTHS) is a novel cyber-physical testing technique for investigating especially large or complicated structural systems. Controllers are required to compensate for the dynamics of transfer systems that emulate the interactions between the physical and numerical substructures. Thus, both the stability and accuracy of RTHS testing highly depend on the effectiveness of the control strategy. This paper proposes a robust sliding mode controller (SMC) as a transfer system control strategy in RTHS. A design procedure of the SMC control strategy is presented. The benchmark problem on RTHS control is utilized for demonstration and validation of SMC for this class of problems. Virtual RTHS results show that the SMC strategy significantly improves the performance and robustness of RTHS testing.

Nonlinear energy sink (NES) devices have recently been introduced as a means of passive structural control and have been shown to effectively dissipate energy from structural systems during extreme vibrations. Due to their essential geometric nonlinearities, time domain based methods are often applied for identifying their system parameters, which is a challenging task. The unscented Kalman filter (UKF) has been shown in numerical studies to be robust to highly nonlinear systems with noisy data and therefore presents a promising option for identification. In this study, the UKF is used to determine the model parameters of an experimental NES device whose behavior is governed by a geometric nonlinearity in its stiffness and a friction-based nonlinearity in its damping. The standard implementation of the UKF is compared with two implementation methods developed by the authors, which vary in their use of experimental responses to train the NES device model. The impact of choosing different prior distributions on the parameters is also analyzed through Latin hypercube sampling to enhance the quality of the identification for practical implementation, where the prior distribution on the parameters is often ill-defined. The identified models generated using one of the proposed UKF implementation methods is shown to provide a robust model of the NES, demonstrating that the UKF can be used for parameter identification with this class of devices.

This paper presents the problem definition and guidelines for a benchmark control problem in real-time hybrid simulation for a seismically excited building, to appear in a Special Issue of Mechanical Systems and Signal Processing. Benchmark problems have been especially useful in enabling a community of researchers to leap forward on a given topic, distill the lessons learned, and identify the capabilities and limitations of various approaches. The focus here is on the design of an effective transfer system displacement tracking controller which is a commonly used approach for ensuring that interface conditions between numerical and experimental substructures are satisfied. In this study, a laboratory model of a three-story steel frame is considered as the reference structure. Realistic numerical models are developed and provided to represent the numerical and experimental substructures and the transfer system, which is comprised of hydraulic actuation, sensing instrumentation, and control implementation hardware. Experimental components are identified and provided as Simulink models, which are executed in real-time using Simulink Desktop Real-Time capability to enable realistic virtual real-time hybrid simulation. The task of each participant of the Special Issue is to design, evaluate, and report on their proposed controller approaches using the numerical models and computational codes provided. Such approaches will be assessed for robustness and performance using the provided tools. This benchmark problem is expected to further the understanding of the relative merits, as well as provide a clear basis for evaluating the performance of various control approaches and algorithms for RTHS. To illustrate some of the design challenges, a sample control strategy employing a proportional-integral (PI) controller is included, in addition to the built-in control loop of the transfer system.

In a typical Real-time Hybrid Simulation (RTHS) setup, servo-hydraulic actuators serve as interfaces between the computational and physical substructures. Time delay introduced by actuator dynamics and complex interaction between the actuators and the specimen has detrimental effects on the stability and accuracy of RTHS. Therefore, a good understanding of servo-hydraulic actuator dynamics is a prerequisite for controller design and computational simulation of RTHS. This paper presents an easy-to-use parametric identification procedure for RTHS users to obtain re-useable actuator parameters for a range of payloads. The critical parameters in a linearized servo-hydraulic actuator model are optimally obtained from genetic algorithms (GA) based on experimental data collected from various specimen mass/stiffness combinations loaded to the target actuator. The actuator parameters demonstrate convincing convergence trend in GA. A key feature of this parametric modeling procedure is its re-usability under different testing scenarios, including different specimen mechanical properties and actuator inner-loop control gains. The models match well with experimental results. The benefit of the proposed parametric identification procedure has been demonstrated by (1) designing an H ∞ controller with the identified system parameters that significantly improves RTHS performance; and (2) establishing an analysis and computational simulation of a servo-hydraulic system that help researchers interpret system instability and improve design of experiments.

Model updating procedures are traditionally performed off-line. With the significant recent advances in embedded systems and the related real-time computing capabilities, online or real-time, model updating can be performed to inform decision making and controller actions. The applications for real-time model updating are mainly in the areas of (i) condition diagnosis and prognosis of engineering systems; and (ii) control systems that benefit from accurate modeling of the system plant. Herein, the development of a cyber-physical real-time model updating experimental platform, including real-time computing environment, model updating algorithm, hardware architecture and testbed, is described. Results from two challenging experimental implementations are presented to illustrate the performance of this cyber-physical platform in achieving the goal of updating nonlinear systems in real-time. The experiments consider typical nonlinear engineering systems that exhibit hysteresis. Among the available algorithms capable of identification of such complex nonlinearities, the unscented Kalman filter (UKF) is selected for these experiments as an effective method to update nonlinear dynamic system models under realistic conditions. The implementation of the platform is discussed for successful completion of these experiments, including required timing constraints and overall evaluation of the system.

Highway sign support structures enhance traffic safety by allowing messages to be delivered to motorists related to directions and warning of hazards ahead, and facilitating the monitoring of traffic speed and flow. These structures are exposed to adverse environmental conditions while in service. Strong wind and vibration accelerate their deterioration. Typical damage to this type of structure includes local fatigue fractures and partial loosening of bolted connections. The occurrence of these types of damage can lead to a failure in large portions of the structure, jeopardizing the safety of passing traffic. Therefore, it is important to have effective damage detection approaches to ensure the integrity of these structures. In this study, an extension of the Angle-between-String-and-Horizon (ASH) flexibility-based approach [32] is applied to locate damage in sign support truss structures at bay level. Ambient excitations (e.g. wind) can be considered as a significant source of vibration in these structures. Considering that ambient excitation is immeasurable, a pseudo ASH flexibility matrix constructed from output-only derived operational deflection shapes is proposed. A damage detection method based on the use of pseudo flexibility matrices is proposed to address several of the challenges posed in real-world applications. Tests are conducted on a 17.5-m long full-scale sign support truss structure to validate the effectiveness of the proposed method. Damage cases associated with loosened bolts and weld failures are considered. These cases are realistic for this type of structure. The results successfully demonstrate the efficacy of the proposed method to locate the two common forms of damage on sign support truss structures instrumented with a few accelerometers.

System identification is becoming more important in structural dynamic applications including structural model update, damage detection primarily due to the rapid increase in the number of damaged or deteriorated engineering structures, and load identification for remaining service life forecasting. Time-domain identification techniques based on measured vibration data, e.g. the least-squares estimation (LSE), have been studied for a relatively long time and shown to be useful. However, the traditional least-squares techniques require that all the external excitation measurements should be available, which may not be the case for many practical applications. In this paper, by introducing a weighted positive definite matrix to the objective function and a learning coefficient using the information in the previous iterations in the identification approach to improve the convergence performance, an alternative iterative approach for both structural parameters and dynamic loading identification, referred to as weighted adaptive iterative least-squares estimation with incomplete measured excitations (WAILSE-IME), was proposed. The accuracy, convergence, and robustness of the proposed approach was demonstrated via numerical simulation on a six-story shear building model with noise-free and different levels of noise-polluted structural dynamic response measurements. The effects of the positive weight matrix, the learning coefficient, and the sampling duration on the convergence and the accuracy of the proposed approach were discussed and the results were compared with available related literature results. Results show that the proposed approach can simultaneously identify structural parameters and unknown excitations within very limited iterations with high accuracy and shows its robustness even noise-polluted dynamic response measurements are utilized. Furthermore, the approach was validated via available experimental measurements for a four-story frame structure excited by impact excitations. The results show that the proposed approach is capable of identifying both structural parameters and the unmeasured excitations with acceptable accuracy and improved convergence.