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Browsing by Author "Matheu, Enrique E."

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    Active and Semi-Active Control of Civil Structures under Seismic Excitation
    Matheu, Enrique E. (Virginia Tech, 1997-05-06)
    The main focus of this study is on the active and semi-active control of civil engineering structures subjected to seismic excitations. Among different candidate control strategies, the sliding mode control approach emerges as a convenient alternative, because of its superb robustness under parametric and input uncertainties. The analytical developments and numerical results presented in this dissertation are directed to investigate the feasibility of application of the sliding mode control approach to civil structures. In the first part of this study, a unified treatment of active and semi-active sliding mode controllers for civil structures is presented. A systematic procedure, based on a special state transformation, is also presented to obtain the regular form of the state equations which facilitates the design of the control system. The conditions under which this can be achieved in the general case of control redundancy are also defined. The importance of the regular form resides in the fact that it allows to separate the design process in two basic steps: (a) selection of a target sliding surface and (b) determination of the corresponding control actions. Several controllers are proposed and extensive numerical results are presented to investigate the performance of both active and semi-active schemes, examining in particular the feasibility of application to real size civil structures. These numerical studies show that the selection of the sliding surface constitutes a crucial step in the implementation of an efficient control design. To improve this design process, a generalized sliding surface definition is used which is based on the incorporation of two auxiliary dynamical systems. Numerical simulations show that this definition renders a controller design which is more flexible, facilitating its tuning to meet different performance specifications. This study also considers the situation in which not all the state information is available for control purposes. In practical situations, only a subset of the physical variables, such as displacements and velocities, can be directly measured. A general approach is formulated to eliminate the explicit effect of the unmeasured states on the design of the sliding surface and the associated controller. This approach, based on a modified regular form transformation, permits the utilization of arbitrary combinations of measured and unmeasured states. The resulting sliding surface design problem is discussed within the framework of the classical optimal output feedback theory, and an efficient algorithm is proposed to solve the corresponding matrix nonlinear equations. A continuous active controller is proposed based only on bounding values of the unmeasured states and the input ground motion. Both active and semi-active schemes are evaluated by numerical simulations, which show the applicability and performance of the proposed approach.
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    Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems
    Liut, Daniel Armando (Virginia Tech, 1999-08-18)
    The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO).