Manlan Liu

To address the strong coupling between trolley motion and payload swing, as well as the difficulty of determining optimal braking timing during emergency operations of overhead cranes in complex environments, a model-predictive braking control method integrated with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed. Within the Model Predictive Control (MPC) framework, payload swing angle constraints are explicitly incorporated, and an adaptive braking reference trajectory is constructed to achieve rapid and stable stopping while effectively suppressing load oscillations. Furthermore, the TD3 algorithm is employed for online adaptive optimization of key MPC parameters, enabling a dynamic trade-off between braking performance and swing suppression under varying operating conditions. In addition, a minimum braking distance prediction model based on Support Vector Regression (SVR) is developed, and a state-dependent safety-critical region prediction model is established to quantitatively determine optimal braking timing. Simulation results across multiple operating conditions demonstrate that the proposed TD3–MPC method outperforms conventional MPC in terms of braking efficiency, swing suppression capability, and system stability while satisfying swing angle constraints. Moreover, real-crane experimental results demonstrate the effectiveness of the proposed safety-critical region prediction method in determining appropriate braking trigger timing and achieving safe and smooth stopping of the overhead crane under obstacle-avoidance conditions.

The construction of rational absolute nodal coordinate formulation (RANCF) elements is usually based on a linear transformation of non-uniform rational B-spline (NURBS) geometry. However, this linear transformation can lead to property transfer issues, which greatly reduce the modeling efficiency, especially for conic sections. To overcome this limitation, we first analyze the geometric constraints of conic sections and derive a unique defining equation in rational parametric form. A corresponding degree-elevation formula is also obtained. Using these results, we propose a direct definition method for RANCF elements that explicitly exploits the analytic properties of conic sections. The method provides fast and accurate expressions for the nodal coordinates and weights, and thus enables efficient modeling of RANCF elements for conic-section configurations. We also mitigate the arbitrariness in element definition by introducing, for the first time, the concept of a mapping factor K, which characterizes the mapping between the physical space and the parameter space. Based on this mapping factor, we establish a parameterization procedure for RANCF conic-section elements. An evaluation criterion for K is further proposed and used to define the optimal mapping factor K_opt, which yields an optimal parameterization and allows the construction of K_opt elements. Numerical examples demonstrate that, in large-deformation analyses of flexible systems, the proposed elements can achieve a given accuracy with fewer elements than conventional approaches.