针对直线AMD行程受限的问题，引入“旋转”的概念，结合直线电磁AMD驱动方式,提出了电磁驱动的回转式振动控制装置(RVCD)。RVCD利用质量块在轨道上做回转运动为结构提供主动控制力，从而减小土木工程结构在服役过程中受到外界干扰时的振动响应。由拉格朗日方程，建立了结构和装置耦合系统的力学模型，通过无量纲化处理以及全局坐标变换，将其转化为一种简化的级联规范型系统，采用滑模控制算法设计了该非线性系统的控制器。通过仿真，验证了结构RVCD控制系统在参数存在不确定性和受到外界干扰时良好的控制效果和鲁棒性。同时仿真结果表明，该控制器的减振效果对装置物理参数的改变具有不敏感性，并能够有效解决直线AMD的行程问题。最后，针对滑模控制算法中多参数的取值问题，从AMD做负功的角度构造了目标函数，提出了一种基于粒子群算法的优化方法。优化后结果表明，RVCD能够有效承担输入到结构的能量，从而减轻结构自身的地震响应。
Abstract
Here, aiming at the problem of limited stroke of linear AMD, the concept of “rotary” was introduced.Combined with the driving mode of linear electromagnetic AMD, the electromagnetic driven rotary vibration control device (RVCD) was proposed.RVCD could provide active control force to a structure by using a mass block rotating on its orbit to reduce vibration response of a civil engineering structure under external interferences during its service.Based on Lagrange equation, the mechanical model of the structure-RVCD coupled system was established.Through the dimensionless treatment and global coordinate transformation, the system was converted into a simplified cascade canonical system.The sliding mode control algorithm (SMCA) was used to design this nonlinear system’s controller.The numerical simulation verified the good control effect and robustness of the structural-RVCD coupled system under conditions of uncertain parameters and external disturbances.The simulation results showed that the vibration reduction effect of the controller is insensitive to variation of RVCD physical parameters, RVCD can effectively solve the stroke problem of linear AMD.Finally, aiming at the problem of multi-parameter choosing in SMCA, the objective function was constructed from the perspective of AMD doing negative work, and an optimization method based on PSO was proposed.The optimized results showed that RVCD can effectively bear input energy of a civil engineering structure to reduce its seismic responses.
关键词
回转式振动控制装置（RVCD） /
滑模控制算法(SMCA) /
粒子群算法(PSO) /
参数优化
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Key words
rotary vibration control device (RVCD) /
sliding mode control algorithm (SMCA) /
particle swarm optimization (PSO) /
parametric optimization
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