Semi-active model predictive control for 3rd generation benchmark problem using smart dampers

A semi-active strategy for model predictive control (MPC), in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.     

高层建筑横风向反应的模糊神经网络控制

介绍了第3代结构风振控制基准问题的定义。通过观测部分楼层加速度和控制力输出,建立了模糊神经网络控制器,解决了传统控制中有限的传感器数目对系统振动状态估计的困难;利用模糊神经网络预测结构的控制行为,消除了闭环控制系统中存在的时滞;通过模糊神经网络控制器的学习功能,解决了土木工程复杂结构模糊控制中难以依据专家的主观经验来确定模糊控制规则和语言变量隶属函数等困难。以风振控制的基准问题为研究对象,编制了程序对受控系统进行数值仿真分析。分析表明,模糊神经网络控制策略能有效地抑制高层建筑的风振反应。