Real-time structural dynamics estimation in hydraulically actuated systems using 3D flexible multibody simulation and SLIDE networks

The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear structural dynamics. This, in turn, makes control more difficult, simulation more computationally intensive, and achieving real-time autonomy, using standard approaches, impossible. Machine learning through data-driven, physics-informed and physics-inspired networks, however, promises more computationally efficient and accurate solutions to nonlinear dynamic problems. This study proposes a physics-inspired real-time structural dynamics estimation framework using novel SLIDE-neural networks for the hydraulically actuated three-dimensional systems. 1 1 GitHub source, https://github.com/qkhadim22/SLIDE-EstimatingStructuralDeflection.git . It learns the dynamics of a system by utilizing physically known damping properties in a SLIDE window. For data acquisition, an algorithm is introduced from randomized initial configurations and hydraulic pressures in a system, along with a method to compute the SLIDE size. The new framework was evaluated across varying geometries, and 1-DOF and 2-DOF hydraulically actuated systems. Different sensor configurations while lifting various payloads were also tested. The SLIDE-trained network accelerated structural dynamics estimation solutions by a factor of 1 0 3 in reference to flexible multibody simulation batches and provided reasonable accuracy. Performance of new framework is compared with the sequential architectures such as RNN, LSTM and CNN. The SLIDE network was successfully trained in less time using standard parameters from PyTorch, ADAM optimizer. These results support the studies goal of providing robust, real-time solutions for control, robotic manipulators, structural health monitoring, and automation problems. • Physics-inspired framework estimating structural dynamics in flexible systems. • Efficient data acquisition approach from random initial configurations of systems. • A method to compute SLIDE window t d without the equations of motion. • Validation across geometries, 1-DOF and 2-DOF systems, and sensor configurations. • Comparison with state-of-the-art sequential models: RNNs, LSTMs, and CNNs.