Real-Time State Estimation of Hydraulically-Driven Systems Based on Unscented Kalman Filter and Low-Fidelity Models

Abstract

Abstract

The algorithms identifying machine health and predicting maintenance needs require accurate information about the machine’s state. Because of the great amount of collected data and limited data buffering and data transfer capabilities in many hydraulic machinery applications, the data should be processed in real time. The real-time requirement demands computationally efficient simulation models, while the self-correcting nature of estimation algorithms allows models with lower precision to be used.

The study combines the novel low-fidelity surrogate models with an Unscented Kalman Filter (UKF) for the real-time state estimation of the coupled mechanical systems. The surrogate-assisted modeling approach reduces the model complexity and improves computational efficiency while maintaining high accuracy. A hydraulic forestry crane case study is investigated, and the computational efficiency and numerical accuracy of the developed observers are evaluated. The encoder measurements are provided by the high-fidelity model. The high-fidelity model introduces imperfections in the form of the frictional forces in the hydraulic cylinders, which induce approximately 2 % error in actuated force. The case study results demonstrate that the surrogate-based state observer delivers estimations within the real-time computational range. It shows a maximum accuracy deviation of 7.31 % for unmeasured states compared to the high-fidelity model-based observer.