Abstract：The hierarchical decentralized control problem of building structure vibration control is studied in this paper. Firstly, The associated coupling between subsystems is eliminated by setting the global controller. Secondly， Lyapunov stability theory and RBF neural network theory are employed to design the adaptive control law which depends only on the displacement and the velocity response of relevant subsystem, and the parameters of adaptive RBF neural network local sub-controller is optimized through using the differential evolution (DE) algorithm. And then, the adaptive RBF neural network hierarchical decentralized control (ARBFHDC) algorithm is established for the building structure vibration control. The ASCE 9-story Benchmark building is selected as a numerical example to evaluate the control performances of the hierarchical decentralized control. Numerical simulation results indicate that the ARBFHDC algorithm is suitable for hierarchical decentralized control strategy under different seismic excitations, it can perform up to a superior control performance when comparing with traditional centralized control, and can guarantee the actuators of each subsystem are operating at maximum efficiency.
潘兆东 谭平 刘良坤 周福霖 . 基于自适应RBF神经网络算法的建筑结构递阶分散控制研究[J]. 土木工程学报, 2018, 51(1): 51-57.
Pan Zhaodong Tan Ping Liu Liangkun Zhou Fulin. Hierarchical decentralized control of building structure based on adaptive RBF neural network algorithm. 土木工程学报, 2018, 51(1): 51-57.
