TD3-Enhanced MPC for Safe Braking of Overhead Cranes with Safety-Critical Region Prediction

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.