Semi-active switching vibration control with tree-based prediction and optimization strategy

Abstract

A novel control strategy that combines model predictive control (MPC) with semi-active vibration control that uses a highly effective, energy-efficient, and stable piezoelectric transducer is proposed in this paper. Incorporating MPC into semi-active vibration control enables significant improvements in control performance and robustness. However, it is challenging to directly predict and optimize the input trajectory because the semi-active input has a state-dependent discontinuous nature. To realize effective optimal control, we need a strategy that can predict the discontinuous semi-active input trajectory in a reasonable manner and is computationally cost-efficient. The proposed method employs a prediction algorithm based on a tree data structure. The proposed algorithm achieves flexible prediction and optimization of a semi-active input trajectory with a simple tree traversal. In addition, the proposed method employs a switching criterion to minimize the computational cost and implement fast prediction and optimization. The proposed method is called predictive switching vibration control with tree-based formulation and optimization, or the PSTFO method. The simulation proved that the proposed PSTFO method can predict discontinuous semi-active input and realizes optimal vibration control performance and high robustness. In addition, the high control performance and robustness of the proposed method were experimentally validated.