Neuro-control of cable vibration using semi-active magneto-rheological dampers

Abstract

A neuro-control method is proposed for semi-active vibration control of stay cables using magneto-rheological (MR) dampers. A finite element model with multiple degrees-of-freedom is formulated for the hybrid system of a sagged stay cable incorporated with MR dampers. Two neural network control strategies are developed by using the full-order system model directly and introducing a reduced-order modal model, respectively. In the first strategy, which eschews a model reduction process, the neural network controller is designed and trained to have the dual functions of controller and observer, so that the resulting neuro-controller is able to perform the clipped linear quadratic Gaussian (LQG) control with incomplete state observation. In the second strategy, with the aid of a reduced-order modal model, the neural network controller is designed to consist of a control network and a state estimator network. The resulting neuro-controller performs the clipped LQG control using only a few observed states. The control effectiveness of the proposed neural network control strategies is verified numerically by application to a 12 m long scale-model cable prototype transversely connected with an MR damper near the lower anchorage. The response mitigation ability of the proposed control strategies using incomplete output observation is compared with that of the clipped LQG control with full state observation. The adaptive capability of the configured neuro-controllers under dynamic excitation distinct from training loading is examined. The analysis results show that the proposed control strategies can effectively implement semi-active vibration control of stay cables with the use of MR dampers.