控制理论(社会学)
执行机构
补偿(心理学)
计算机科学
补偿方式
自适应控制
植物
反向
控制工程
工程类
控制(管理)
数学
人工智能
心理学
几何学
精神分析
数字营销
万维网
营销投资回报率
作者
Cheng Chen,James M. Ricles,Tong Guo
出处
期刊:Journal of Engineering Mechanics-asce
[American Society of Civil Engineers]
日期:2012-04-13
卷期号:138 (12): 1432-1446
被引量:53
标识
DOI:10.1061/(asce)em.1943-7889.0000450
摘要
Real-time hybrid simulation provides an economical and efficient experimental technique for performance evaluation of structures under earthquakes. A successful real-time hybrid simulation requires accurate actuator control in order to achieve reliable experimental results. The time delay as a result of servohydraulic dynamics, if not compensated for properly, would lead to inaccurate or even unstable simulation results. However, the nonlinearities in servohydraulic systems and experimental substructures make the actuator delay difficult to accurately estimate in practice. Therefore, actuator control presents a challenge for the application of the real-time hybrid simulation technique to earthquake engineering research. This paper presents an improved adaptive inverse compensation technique for real-time hybrid simulation. Two adaptive control laws based on a synchronization subspace plot are introduced to adjust the compensation parameters in order to minimize both phase and amplitude errors in the servohydraulic actuator response. The improved adaptive inverse compensation method is experimentally evaluated through real-time tests involving a large-scale magneto-rheological damper subjected to band-limited white noise–generated random displacements and variable current inputs. The experimental results are compared with the command displacements, with the error assessed using various evaluation criteria. The improved adaptive inverse compensation is compared with an existing adaptive inverse compensation method to demonstrate the improvement that the newly developed compensation method offers in minimizing actuator delay. The proposed improved adaptive inverse compensation method is demonstrated to further improve actuator control by reducing not only actuator tracking errors but also associated energy errors.
科研通智能强力驱动
Strongly Powered by AbleSci AI