The magnetorheological (MR) damper is a newly developed semiactive control device that possesses unique advantages such as low power requirement and adequately fast response rate. The device has been previously tested in a laboratory to determine its dynamic properties and characterized by a system of nonlinear differential equations. This paper presents an alternative representation of the damper in terms of a multilayer perceptron neural network. A neural network model with 6 input neurons, one output neuron and twelve neurons in the hidden layer is used to simulate the dynamic behavior of the MR damper. Training of the model is done by a Gauss-Newton based Levenberg-Marquardt method using data generated from the numerical simulation of the nonlinear differential equations. An optimal brain surgeon strategy is adopted to prune the weights and optimize the neural networks. An optimal neural network is presented that satisfactorily represents dynamic behavior of the MR damper.
A comparative analytical and experimental study of several algorithms for the control of
seismically excited floor- and base-isolated structures is pursued in the current study. A
hybrid isolation system that is comprised of a bidirectional roller–pendulum system (RPS)
and augmented by controllable magnetorheological (MR) dampers is proposed to reduce
the potential for damage to structures and sensitive equipment. Bidirectional motions are
intelligently ameliorated in real time by the modulation of MR damper resistance. A
Bouc–Wen model is adopted in numerical and experimental trials to predict behavior of the
MR dampers. Three contrasting control techniques are examined. They include
neural network control, LQR/clipped optimal control with variable gains and fuzzy
logic control. Each control scheme is a candidate for mitigating the response of a
superstructure to near- and far-field seismic loadings. Minimization of displacement and
acceleration responses of the structure are considered in the formulation of each
approach to control. Results of the numerical and large-scale experimental efforts
reveal that the response of the isolated structure is effectively alleviated by all of
the considered control methods, although they do not perform equally well. The
LQR/clipped optimal controller with variable gains is superior to the other controllers in
50% of the investigated cases, while the fuzzy logic controller performs well for
earthquakes with large accelerations. Neural network control is found to be effective in
minimizing the acceleration of the superstructure that is subject to moderate
excitation.
Tall, slender structures and long bridges inherit numerous uncertainties due to model errors, stress calculations, material properties, and load environments and may undergo large forces from natural hazards such as earthquakes and strong wind events. This paper develops a robust active control approach with para metric uncertainty in the system and control input and unstructured uncertainty in the disturbance input ma trices based on an uncertain structural system. A special single-valued decomposition (SVD) is applied to structured uncertain structures. The robust control law provides robust relative stability, an H∞-norm distur bance attenuation, and H2 optimality. The H∞ norm of the transfer function from the external disturbance forces (e.g., earthquake, wind, etc.) to the observed system states is restricted by a prescribed attenuation in dex δ. Preservation of robust H2 optimality of uncertain structural systems is discussed. This paper considers both structured uncertainties and norm-bounded unstructured uncertainties. Numerical simulations that use the robust controller show significant reduction in vibrations.
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