A neural state-space-based model predictive technique for effective vibration control in nano-beams

Abstract

Model predictive control (MPC) is a cutting-edge control technique, but its susceptibility to inaccuracies in the model remains a challenge for embedded systems. In this study, we propose a data-driven MPC framework to address this issue and achieve robust and adaptable performance. Our framework involves systematically identifying system dynamics and learning the MPC policy through function approximations. Specifically, we introduce a system identification method based on the Deep neural network (DNN) and integrate it with MPC. The function approximation capability of DNN enables the controller to learn the nonlinear dynamics of the system then the MPC policy is established based on the identified model. Also, through an added control term the robustness and convergence of the closed-loop system are guaranteed. Then the governing equation of a non-local strain gradient (NSG) nano-beam is presented. Finally, the proposed control scheme is used for vibration suppression in the NSG nano-beam. To validate the effectiveness of our approach, the controller is applied to the unknown system, meaning that solely during the training phase of the neural state-space-based model we relied on the data extracted from the time history of the beam’s deflection. The simulation results conclusively demonstrate the remarkable performance of our proposed approach in effectively suppressing vibrations.