Research on Leveling Control for Vehicle-Mounted Stewart Platforms

To address the safety concerns of incapacitated patients caused by changes in vehicle pose during the operation of an autonomous rescue vehicle on an unstructured road surface, this paper proposes an active leveling control scheme based on the Stewart platform. First, a complete kinematic and dynamic model of the Stewart platform and a double-layer platform leveling control model were established. Subsequently, a non-singular terminal sliding-mode control (NTSMC) algorithm based on a radial basis function (RBF) neural network was designed. By using the neural network to approximate aggregate uncertainties online, high-precision control of the Stewart platform was achieved. Additionally, to enhance perception capabilities in dynamic environments, an ORB-SLAM3 algorithm was proposed that integrates the YOLO11n-Seg instance segmentation algorithm. This approach effectively filters out dynamic feature points, enabling robust vehicle pose estimation. Finally, a physical double-layer Stewart platform experimental system was constructed to comprehensively validate the proposed control and vision algorithms.