Research on sliding mode decentralized control based on adaptive learning rate RBF neural network for large-scale engineering structures

This paper Proposes a decentralized adaptive learning rate RBF neural network sliding mode control (DALRBFSMC) algorithm for dealing with the influence and the uncertainty of the interaction forces between subsystems and the external loads. Lyapunov stability theory is employed to design the decentralized sliding mode control law which depends only on the displacement and the velocity response of relevant subsystems. Combined with RBF neural network theory and the classical gradient descent method, the adaptive learning rate of RBF network weights-adjustment is derived by using a Lyapunov function. And then the decentralized adaptive learning rate RBF neural network sliding mode control (DALRBFSMC) is designed, which can adjust the switching gain of the sliding mode control law in real time. An ASCE 9-story benchmark building is selected as a numerical example to evaluate the control performances of decentralized control and centralized control. Numerical simulation results indicate that the DALRBFSMC algorithm is suitable for different decentralized control strategy, and that overlapping decentralized control can perform up to a superior control performance when comparing with traditional centralized control, and also guarantee each of the actuators to be operating at maximum efficiency. © 2019, Engineering Mechanics Press. All right reserved.