Active Interaction Control of Tall Buildings Subjected to Near-Field Ground Motions

2002 ◽  
Vol 128 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Yunfeng Zhang ◽  
Wilfred D. Iwan
2016 ◽  
Vol 2016 ◽  
pp. 1-19
Author(s):  
Ming-Hsiang Shih ◽  
Wen-Pei Sung

The intensity of natural disasters has increased recently, causing buildings’ damages which need to be reinforced to prevent their destruction. To improve the seismic proofing capability of Accumulated Semiactive Hydraulic Damper, it is converted to an Active Interaction Control device and synchronous control and predictive control methods are proposed. The full-scale shaking table test is used to test and verify the seismic proofing capability of the proposed AIC with these control methods. This study examines the shock absorption of test structure under excitation by external forces, influences of prediction time, stiffness of the auxiliary structure, synchronous switching, and asynchronous switching on the control effects, and the influence of control locations of test structure on the control effects of the proposed AIC. Test results show that, for the proposed AIC with synchronous control and predictive control of 0.10~0.13 seconds, the displacement reduction ratios are greater than 71%, the average acceleration reduction ratios are, respectively, 36.2% and 36.9%, at the 1st and 2nd floors, and the average base shear reduction ratio is 29.6%. The proposed AIC with suitable stiffeners for the auxiliary structure at each floor with synchronous control and predictive control provide high reliability and practicability for seismic proofing of buildings.


Author(s):  
Wei Meng ◽  
Quan Liu ◽  
Zude Zhou ◽  
Qingsong Ai

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value – Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.


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