Top PDF Semi-Active Control of Dynamically Excited Structures Using Active Interaction Control

Semi-Active Control of Dynamically Excited Structures Using Active Interaction Control

Semi-Active Control of Dynamically Excited Structures Using Active Interaction Control

106 3.32 Distribution of a Number of Attachment, b Control Force, c AS Displacement Relative to the Support Floor, d AS Absolute Acceleration in the 20-story Building Under the SCH Groun[r]

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Semi-Active Control of Structures Using a Neuro-Inverse Model of MR Dampers

Semi-Active Control of Structures Using a Neuro-Inverse Model of MR Dampers

Abstract. A semi-active controller-based neural network for a 3 story nonlinear benchmark structure equipped with a Magneto Rheological (MR) damper is presented and evaluated. An inverse neural network model (NIMR) is constructed to replicate the inverse dynamics of the MR damper. A Linear Quadratic Gaussian (LQG) controller is also designed to produce the optimal control force. The LQG controller and the NIMR models are linked to control the structure. The eectiveness of the NIMR is illustrated and veried using the simulated response of a full-scale, nonlinear, seismically excited, 3-story benchmark building excited by several historical earthquake records. The semi-active system using the NIMR model is compared to the performance of an active LQG and a Clipped Optimal Control (COC) system, which is based on the same nominal controller as used in the NIMR damper control algorithm. Two passive control systems are also considered and compared. The results demonstrate that by using the NIMR model, the MR damper force can be commanded to follow closely the desirable optimal control force. The results also show that the control system is eective, and achieves better performance than active LQG and COC system. The optimal passive controller performs better than the NIMR. However, the performance of NIMR will be improved if a more eective active controller is replaced by a LQG controller.
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Semi-Active LQG Control of Seismically Excited Nonlinear Buildings using Optimal Takagi-Sugeno Inverse Model of MR Dampers

Semi-Active LQG Control of Seismically Excited Nonlinear Buildings using Optimal Takagi-Sugeno Inverse Model of MR Dampers

effective and reliable control. This is evident by achieving better performance in many indices using the proposed optimal T-S fuzzy inverse model. In addition, for nonlinear structures with limited state feedback such as the 20-storey benchmark building model in this study, if a more efficient primary optimal controller is designed, proposed FIMM will perform better.

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Performance of Fixed-Parameter Control Algorithms on High-Rise Structures Equipped with Semi-Active Tuned Mass Dampers

Performance of Fixed-Parameter Control Algorithms on High-Rise Structures Equipped with Semi-Active Tuned Mass Dampers

To this date, most of the tuning of the mechanical parameters of a TMD device is achieved via closed- form expressions derived from the minimization of the rms acceleration response of a single degree of freedom (SDOF) subjected to white noise or harmonic excitation. While this approach is broadly ac- cepted, representing civil engineering structures with an equivalent SDOF system can lead to signi fi - cant errors in the estimation of their dynamic response. The problem ampli fi es when one considers the probabilistic nature of the knowledge of the system ’ s properties and the fact that the estimated proper- ties can vary with time (e.g. amplitude dependence, fl uid – structure interaction etc.). Moreover, obtaining TMD mechanical parameters through the use of harmonic or fl at spectrum inputs may not always yield optimum values (Ricciardelli et al., 2000). For these reasons, in this paper, because the motion of long period structures is generally governed by the fi rst modal response, both the TMD and STMD are tuned to the fundamental frequency of the structure. On the other hand, tentative damping values are given to the damping devices based on existing formulas found in literature Figure 2. Ensemble of the structural con fi gurations. (left) TMD-equipped structure and (right) STMD-
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Optimization of Semi-Active Control of Seismically Excited Buildings Using Genetic Algorithms

Optimization of Semi-Active Control of Seismically Excited Buildings Using Genetic Algorithms

In this paper, the performance of semi-active uid viscous dampers on the reduction of seismic responses of building structures is optimized using genetic algo- rithms. For this purpose, a realistic 12-story building located in the city of Rash, in Iran, is considered as a numerical problem. Equations of motion of a three-dimensional (3-D) model of the building, with added semi-active uid viscous dampers, are written using an analytical procedure. Semi-active dampers are modeled by a linear spring-dashpot connected in parallel. For time history analysis of the building, 5 earthquake acceleration records are chosen, corrected for base-line errors, ltered for unwanted noise, and scaled based on the IBC 2006 standard. Then, using MATLAB software, the equations are resolved in state- space and the controlled and uncontrolled responses of the building are obtained.
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On the Alternative Approach to Active Control

On the Alternative Approach to Active Control

The main results of this work are the following: 1) transparent supportless un- idirectional sources of acoustical wavelets (Section 2), 2) causal sequence of op- erations to reconcile the normal displacements of the protected surface with the incident waves (Section 3). The presented results are a consequence of the problem formulation with initial conditions and could not be obtained in the widespread stationary monochromatic mathematical model of the problem with complex am- plitudes (magnitude A , frequency ω , phase ϕ , i.e. A exp[ ( i t ω ϕ + )] ) of the fields (like in [2] for instance). The monochromatic representation leaves out of view some important situations, such as spatio-temporal labyrinths, where the control algorithm (like “Maxwell’s demon”) operates extremely quickly on the spatial microscales, and this leads to macroscopic results for long slow waves [3].
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Active Control of Irregular Buildings Considering Soil-Structure Interaction Effects

Active Control of Irregular Buildings Considering Soil-Structure Interaction Effects

This paper analyzes the soil-structure interaction (SSI) effect on vibration control effectiveness of active tendon systems for an irregular building, modeled as a torsionally-coupled (TC) structure, subjected to base excitations such as those induced by earthquakes. The SSI effect is governed by the slenderness ratio of superstructure and by the stiffness ratio of soil to superstructure. An H ∞ direct output feedback control algorithm that uses minimization of the entropy is implemented to reduce the seismic responses of TC structures. The control forces are calculated directly by multiplying output measurements by a pre-calculated frequency independent and time-invariant feedback gain matrix which is obtained based on a fixed-base model. Numerical simulations show that the required number of sensors and actuators and their locations highly depend on the degree of floor eccentricity. For a large two-way eccentric building, an one-way active tendon system placed at the opposite side of center of resistance (C.R.) can reduce both translational and torsional responses. If the SSI effect is significant, the proposed control system can still reduce the structural responses, but its performance is much worse than that of the corresponding fixed base model. Therefore, the TC and SSI effects should be considered in the design of active control devices, in particular, for a high-rise building founded on soft site. In this paper, an optimal, practical, and cost-effective design procedure for an active tendon system is proposed for the vibration control of irregular buildings under earthquake excitations. INTRODUCTION
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Development of a semi active car suspension control system using magneto rheological damper model

Development of a semi active car suspension control system using magneto rheological damper model

over ER fluid devices in areas such as the yield strength, the required working volume of fluid, and the required power. The operational modes of the MR fluid are presented along with the linear fluid damper, the rotary brake, and the vibration damper. Kordonsky (1996) developed the concept of the MR converter (or valve) and applies the MR converter to create devices such as the MR linear damper, the MR actuator, and the MR seal. Finally Bolter et al. (1997) examined the rules that should be applied when designing the magnetic circuit for MR devices that are working in the different modes of the MR fluid. Bolter also examined the use of permanent magnets in the design of the magnetic circuit to change the operational point of the MR device. When a magnetic field is applied to the fluid, particle chains form, and the fluid becomes a semi-solid and exhibits viscoplastic behaviour similar to that of ER fluid. This controllable change of state with some desirable features such as high strength, good stability, broad operational temperature range and fast response time gives rise to isolation and suspension system applications. MR fluid dampers considered here are semi-active control devices that use MR fluids to produce controllable damping forces. The main objectives of this paper is to develop fuzzy based controllers for semi-active suspension control and to the utilize ability of the MR damper to eliminate and reduce suspension over a wide range. The results reported herein indicate that this semi-active control system is quite effective for suspension control.
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Analysis and control of semi-active suspension system for  a quarter car model

Analysis and control of semi-active suspension system for a quarter car model

Comparison of body acceleration is shown in Table 2, it is seen that, ANFIS is giving an experience of least body acceleration, thus making a ride more comfort for the passenger as compared to the suspension system, which is getting external power source by PID controller and Neural Network. The choice of controller also depends on the use and cost efficiency of the suspension system, external power source can be preferred by any control strategy as each controller provides very less acceleration, but when precision is required as in case of sports vehicles, the controller which provides least body acceleration should be preferred.
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Parameters optimization and semi active control of suspension based on the road friendliness

Parameters optimization and semi active control of suspension based on the road friendliness

In this paper, the control objective is improving the vehicle on road friendliness, the key performance indicator lowering road damage coefficient. It can be seen from the simulation results, compared with the passive suspension, the vehicle road friendliness is better for the optimal fuzzy control, and the tyre dynamic load is reduced, the vehicle driving security and ride comfort also have improved.

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Active Threat Control

Active Threat Control

Active applications and processes are continuously monitored for suspicious behaviors, like: • Copying or moving files in System or Windows folders or limited access disk locations • Executing or injecting code in another processes’ space in order to run with higher privileges • Running files that have been created with information stored in the binary file

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Active interaction control of a rehabilitation robot based on motion recognition and adaptive impedance control

Active interaction control of a rehabilitation robot based on motion recognition and adaptive impedance control

Abstract—Although the electromyography(EMG) signals and interaction force have been widely used in patient cooperative or interactive training, but the conventional EMG based control usually breaks the process into a patient-driven phase and a separate passive phase, which is not desirable. In this research, an active interaction controller based on motion recognition and adaptive impedance control was proposed and implemented on a six-DOFs parallel robot for lower limb rehabilitation. The root mean square (RMS) features of EMG signals integrating with the support vector machine (SVM) classifier was used to online predict the lower limb intention in advance and to trigger the robot assistance. The impedance control strategy was adopted to directly influence the robot assistance velocity and to allow the exercise to follow a physiological trajectory. Moreover, an adaptive scheme learned the muscle activity level in real time and adapted the robot impedance in accordance with patient’s voluntary participation efforts. The experimental results on three healthy subjects demonstrated that not only the lower limb motion intention can be precisely predicted in advance, but also the robot assistance mode was adjustable based on human-robot interaction and muscle activity level of subjects. Comparing with the conventional EMG-triggered assistance methods, such a strategy increases patient’s motivation because the subject’s movement intention, active interaction efforts as well as the muscle activity level changes can be directly reflected in the trajectory pattern and the robot assistance speeds.
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The active control of acoustic impedance

The active control of acoustic impedance

the specific acoustic controlled on Experimental results support the theoretical work presented in this thesis, demonstrating active control of specific acoustic impedance for normally i[r]

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ACTIVE BODY CONTROL SUSPENSION

ACTIVE BODY CONTROL SUSPENSION

The first complete and prepared for production version of alphabet was introduced in 1999 on the top-of-the-line Mercedes-Benz CL-Class in 2010 across wind stabilization perform was introduced.Magic Body management in 2007, the Mercedes-Benz F700 construct introduced the PRE-SCAN suspension, Associate in Nursing early paradigm road scanning suspension, mistreatment sensors, supported Active Body management.In 2013 the new Mercedes-Benz S-Class (W222) introduced the series production version of PRE-SCAN, however with a stereo camera rather than optical device projectors.In 2014 the new C217S-Class auto introduced Associate in nursing update to Magic Body management, known as Active Curve Tilting. This new system permits the vehicle to lean up to two.5 degrees into a flip, almost like the approach a bike leans into a flip.
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Experimental implementation of Pole Placement Techniques for Active Vibration Control of Smart Structures

Experimental implementation of Pole Placement Techniques for Active Vibration Control of Smart Structures

The optimal performance is obtained when the CL pole is 0.85 times the imaginary part of the OL pole locations (i.e. with a large movement of the pole position towards origin on vertical axis); this defect was clearly eliminated (fig 4b). Similar deterioration in performance was observed if the tip load was changed from 0g to 15g (fig 5a). The transition response is not so good. The amplitude of the CL system increases as compared to OL system during initial time steps. Also the CL settling time is large (i.e. 2.5 second ) as compared with the second case ( with CL settling time of 1.3 second) where the imaginary part of the CL pole locations are made 0.85 times the imaginary part of the OL pole locations (fig 5b). Obviously, higher control voltages will be needed in the later case. By constraining the control voltage to a certain magnitude which is available practically, this problem can be solved. By observing the response in frequency domain (fig 5c), it is observed that, although the first mode amplitude is reduced, the second mode gets excited. However, by using optimal location of CL poles, better performance gets resulted (fig 5d).
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Active Suspension vibration control using Linear H-Infinity and optimal control

Active Suspension vibration control using Linear H-Infinity and optimal control

The design of linear suspension controllers that emphasize either passenger comfort or suspension deflection. The controllers in this section are designed using linear H∞ synthesis [13]. As is standard in the H∞ framework, the performance objectives are achieved via minimizing weighted transfer function norms. Weighting functions serve two purposes in the H∞ framework: They allow the direct comparison of different performance objectives with the same norm, and they allow for frequency information to be incorporated into the analysis. A block diagram of the H∞ control design interconnection for the active suspension problem is shown below. The quarter car model shown is used to design active suspension control laws.
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Active noise reduction in double panel structures : decentralized adaptive feedforward control

Active noise reduction in double panel structures : decentralized adaptive feedforward control

Passive control is the traditional method to reduce the sound pressure at a given location. This technique uses an object that will absorb the radiated power of the disturbance source. The wavelength of the noise source must be small compared to the dimensions of the power absorbing object to well function. So this type of noise control works best for high frequencies [3] [4]. For lower frequencies you need larger damping objects which have a larger mass. For fuel efficiency, airplanes have to be as lightweight as possible so this is an unwanted feature. There are also more advanced passive control techniques such as for example a Helmholtz resonator. A Helmholtz resonator can increase the acoustical damping level inside a cavity between two plates. Simulations have shown that this can result in an overall improvement of 8dB in the 50-150 Hz range [5]. The other newer method, active control, uses secondary sources which generate a field that will interfere with the field produced by the primary noise source. This field will cancel the primary field, resulting in a reduced sound pressure. If the secondary sources are placed within half a wavelength of the disturbance signal of the primary source in all directions the field will be cancelled by a considerable amount [6] [7]. Although in most applications a similar setup is not possible, still active noise control performs well, especially at low frequencies. The main advantage compared to passive control is that no heavy objects are required for the reduction in sound pressure.
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Improving bimanual interaction with a prosthesis using semi-autonomous control

Improving bimanual interaction with a prosthesis using semi-autonomous control

placed on a pair of antagonist muscles (e.g. wrist extensor and flexor muscles of the forearm) in order to directly control a prosthesis function (e.g., hand open/close). Therefore, only single degree of freedom (DoF) could be operated at the time, and a switching signal, such as muscle coactivation, had to be used to change the active DoF. As the mechatronic technology advanced, new solu- tions emerged and already in the 80s, there was active research performed on the concepts of under-actuated control mechanisms [4]. However, the translation of re- search efforts to commercial realm lost its initial momen- tum since it was recognized that main problems of upper limb prosthetic systems lie in the limitations of available man-machine interfaces [5, 6]. Namely, the two EMG- channel control although relatively robust, turned out to be slow and tedious when applied to multi-DoF prostheses [7]. Therefore, development of prosthetic hands slowed down and stayed behind the modern robotic technology. In the last decade, prosthetic hands with multi-articulated fingers have been commercially introduced [8]. However, due to their high mechanical complexity and poor, under- developed control interface the overall robustness suf- fered, thus rendering under-actuated prostheses popular to this day [9]. Moreover, the new prostheses focused on replacing the finger function whereas joints such as wrist received only limited attention and continued to provide rather limited, single-axis functionality [8]. Only with the recent commercial introduction of the myoelectric ma- chine learning interfaces [10] a new impulse is given for further development and improvement of the prosthetic hands. Machine learning methods can be used to improve prostheses control as they rely on recording and classify- ing the activity from multiple muscles to directly activate desired DoFs in a coordinated manner [11]. However, their implementation remains challenging due to de- creased robustness
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Semi-active structural control of offshore wind turbines considering damage development

Semi-active structural control of offshore wind turbines considering damage development

Figure 17 shows standard deviation reduction for all seismic records for only nacelle displacement under LC3 loading conditions. For the sake of brevity, the dynamic response reductions for all the seismic records are averaged for each loading condition and presented in Figures 18 and 19. The average of reduction percentage in the standard deviation of dynamic responses for all ground motion records for load cases LC3 and LC4 (stochastic wind and wave loading in conjunction with seismic excitation and damage development) is obtained and plotted in Figure 18. Dashed lines correspond to semi-active tuned mass dampers and solid lines are for passive tuned mass dampers. For the operational condition (Figure 18a), the standard deviation of nacelle displacements reduces by 20% for STMD with 1% mass ratio and this reduction percentage increases to 39% by increasing the mass ratio to 4%. On the other hand, the PTMD with 1% mass ratio leads to only 10% standard deviation reduction, half of its STMD counterpart. It is interesting that the performance of PTMD becomes worse when the mass ratio increases up to 4%, resulting in 10% increase in the standard deviation of deflection. This suggests that increasing the mass ratio of PTMD cannot improve its dynamic performance and even it worsens the dynamic performance due to the controller becoming off-tune as well as the reduction in the natural frequency of system as a result of the additional mass of tuned mass damper. From the results shown in Figure 18, it is concluded that a semi-active mass damper with a mass ratio of 1% shows much better performance than a passive tuned mass damper with a mass ratio of 4% for the case when there is a change in natural frequency of the system. This means that STMD with a very low mass ratio is more effective than a PTMD with a large mass ratio. Similar trends can be observed for base shear force and base moment responses; however, it should be noted that base shear force and base moment experience lower dynamic response reduction with the vibration control devices. For example, the standard deviation of the base shear force shows a maximum of 7% reduction for STMD with a mass ratio of 3%. Therefore, it could be concluded that the considered structural control devices have more influence on nacelle displacement and base overturning moments rather than base shear force.
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Active bandsaw control

Active bandsaw control

The single and three span cutting plate models are then placed in feedback loops, and performance and robustness considered with respect to band speed, bandmill tension, and in the case [r]

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