The use of bone tissue-engineered scaffolds for repairing bone defects has become extremely common. Bone tissue-engineered scaffolds should have good mechanical properties, a pore structure similar to that of natural bone, appropriate biodegradability, and good biocompatibility to provide attachment sites for growth factors and seed cells. They also need to exhibit special functions such as osteoconductivity and osteoinduction. In this study, the mechanical, degradation, and biological properties of bredigite were studied by using a triply periodic minimal surface (TPMS) model structure. Pressure tests on bone tissue-engineered scaffolds showed that the mechanical properties of TPMS scaffolds were significantly better than those of open-rod scaffolds with the same porosity. By analyzing the biological properties, we found that the TPMS model had better protein adsorption ability than the open-rod model, the cells could better adsorb on the surface of the TPMS scaffold, and the proliferation number and proliferation rate of the TPMS model were higher than those of the open-ended rod model.

To address the tracking control problem of the periodic motion fast tool servo system (FTS), we propose a control method that combines adaptive sliding mode control with closed-loop iterative learning control. Adaptive sliding mode control enhances the system’s robustness to external non-repetitive disturbances, and exponential gain iterative learning control compensates for the influence of periodic disturbances such as cutting force. The experimental results show that the proposed iterative learning controller based on adaptive sliding mode control can effectively eliminate the influence of various interference factors, achieve accurate tracking of the FTS system’s motion trajectory within a limited number of iterations, and ensure the stability of the system, which has the advantages of a fast convergence speed, high tracking accuracy, and strong robustness.