Control of civil engineering structures for earthquake hazard mitigation represents a relatively new area of research that is growing rapidly. Control systems for these structures have unique requirements and constraints. For example, during a severe seismic event, the external power to a structure may be severed, rendering control schemes relying on large external power supplies ineffective. Magnetorheological (MR) dampers are a new class of devices that mesh well with the requirements and constraints of seismic applications, including having very low power requirements. This paper proposes a clipped-optimal control strategy based on acceleration feedback for controlling MR dampers to reduce structural responses due to seismic loads. A numerical example, employing a newly developed model that accurately portrays the salient characteristics of the MR dampers, is presented to illustrate the effectiveness of the approach.

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Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction

Semi-active control systems for seismic protection of structures: a state-of-the-art review

The protection of civil structures, including their material contents and human occupants, is without doubt a worldwide priority of the most serious importance. Such protection may range from reliable operation and comfort, on the one hand, to survivability on the other. Examples of such structures leap to one's mind, and include buildings, offshore rigs, towers, roads, bridges, and pipelines. In like manner, events that cause the need for such protective measures are earthquakes, winds, waves, traffic, lightning, and-today, regrettably-deliberate acts. Indications are that control methods will be able to make a genuine contribution to this problem area, which is of great economic and social importance. In this article, we review the rapid developments which have been occurring in the area of controlled civil structures, including full-scale implementations, actuator types and characteristics, and trends toward the incorporation of more modern algorithms and technologies.

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Controlling buildings: a new frontier in feedback

Due to the inherent nonlinear nature of magnetorheological (MR) dampers, one of the challenging aspects for developing and utilizing these devices to achieve high performance is the development of models that can accurately describe their unique characteristics. In this review, the characteristics of MR dampers are summarized according to the measured responses under different conditions. On these bases, the considerations and methods of the parametric dynamic modelling for MR dampers are given and the state-of-the-art parametric dynamic modelling, identification and validation techniques for MR dampers are reviewed. In the past two decades, the models for MR dampers have been focused on how to improve the modelling accuracy. Although the force–displacement behaviour is well represented by most of the proposed dynamic models for MR dampers, no simple parametric models with high accuracy for MR dampers can be found. In addition, the parametric dynamic models for MR dampers with on-line updating ability and the inverse parametric models for MR dampers are scarcely explored. Moreover, whether one dynamic model for MR dampers can portray the force–displacement and force–velocity behaviour is not only determined by the dynamic model itself but also determined by the identification method.

https://iopscience.iop.org/article/10.1088/0964-1726/20/2/023001/pdf

Magnetorheological fluid dampers: a review of parametric modelling

The application of control techniques to offshore wind turbines has the potential to significantly improve the structural response of these systems. A new simulation tool is developed that can be utilized to model passive, semi-active and active structural control systems in wind turbines. Two independent, single degree of freedom (DOF) tuned mass- damper (TMD) devices are incorporated into a modified version of the aero-elastic code FAST (Fatigue, Aerodynamics, Structures and Turbulence). The TMDs are located in the nacelle of the turbine model, with one TMD translating in the fore-aft direction, and the other in the side-side direction. The equations of motion of the TMDs are incorporated into the source code of FAST, yielding a more realistic system for modeling structural control in wind turbines than has previously been modeled. The stiffness, damping and commanded force of each TMD are controllable through the FAST-Simulink interface, and so idealizations of semi-active and active control approaches can be implemented. A parametric study is performed to determine the optimal parameters of a passive single DOF, fore-aft, TMD system in both a barge-type and monopile support structure. The wind turbine models equipped with TMDs are then simulated and the performance of these new systems is evaluated. The results indicate that passive control approaches can be used to improve the structural response of offshore wind turbines. The results also demonstrate the potential for active control approaches. Copyright © 2010 John Wiley & Sons, Ltd.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/we.426

Passive structural control of offshore wind turbines

In this paper physically motivated and nonparametric models are investigated that predict within some tolerance the response of a semiactive electrorheological (ER) damper that was designed, constructed, and tested. The electrorheological damper is a hydraulic device that was designed for applications in vibration control of civil structures. The simplest possible physically motivated phenomenological models are first considered to predict the damper response without and with the presence of electric field. Subsequently, the performance of a multilayer neural network constructed and trained by an efficient algorithm known as the dependence identification algorithm is examined to predict the response of the ER damper. The performance of the neural network is compared to that of the phenomenological models and some conclusions are provided.

Modeling the Response of ER Damper: Phenomenology and Emulation

New bounds for robust stability of continuous and discrete-time systems under parametric uncertainty

Designing controllers with diagnostic capabilities is important as in a feedback control system, detection and isolation of failures is generally affected by the particular control law used. Therefore, a common approach to control and failure diagnosis problems has significant merit. Controllers capable of performing failure diagnosis have additional diagnostic outputs to detect and isolate sensor and actuator faults. A linear such controller is usually called a four-parameter controller. Neural networks have proved to be a very powerful tool in the control systems area, where they have been used in the modelling and control of dynamical systems. In this paper, a neural network model of a controller with diagnostic capabilities (CDC) is presented for the first time. This nonlinear neural controller is trained to operate as a traditional controller, while at the same time it provides reproduction of the failure occurring either at the actuator or the sensor. The cases of actuator and sensor failure are studied independently. The validity of the results is verified by extensive simulations.

Controllers with diagnostic capabilities. A neural network implementation

This paper presents a fast algorithm for the design of robust output feedback controllers for linear uncertain discrete-time systems. The algorithm utilizes a version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method of conjugate directions and minimizes a performance index that includes a linear quadratic regulator (LQR) term to optimize performance and a robustness term based on recently developed bounds. The minimization of only the robustness term which corresponds to the maximization of the uncertainty bound is also studied. The case of unstructured perturbations in A has been the only one studied in the robust controller design literature; the present algorithm introduces a unified approach to both cases of unstructured and structured perturbations in the matrices of a state-space model. For the special case of unstructured perturbations in A only, the algorithm is shown to improve considerably the existing unstructured uncertainty bound. Several examples, including an aircraft cont...

Optimal design of robust controllers for uncertain discrete-time systems

This article presents three models for predicting the response on an electrorheological (ER) fluid damper developed for seismic protection of structures. The proposed ER damper is a semi-active device and the resulting damping forces can be controlled by applying an electric field. First the performance of a simple nonlinear phenomenological model is investigated. Subsequently, a neural network model is evaluated. Finally, a combination of the phenomenological model and the neural network is presented to approximate the response of the ER fluid damper. The performance of the proposed models is validated against experimental data.

Ioannis K. Konstantopoulos

Papers

Modeling the Response of ER Damper: Phenomenology and Emulation

New bounds for robust stability of continuous and discrete-time systems under parametric uncertainty

Controllers with diagnostic capabilities. A neural network implementation

Optimal design of robust controllers for uncertain discrete-time systems

Modeling the Response of An Electrorheological Fluid Damper: Constitutive Models and Neural Networks