Fuzzy Control-based Adaptive Adjustment of Dynamic Stiffness for Stewart Platforms

An adaptive adjusting strategy of Stewart platform dynamic stiffness based on fuzzy control is explored in this paper. The transient response, steady-state accuracy, anti-disturbance and robustness of Stewart platform are improved remarkably. Simulation experiments and data analysis show that compared with traditional fixed stiffness or PID control, this fuzzy control strategy can quickly achieve steady state under various operating conditions, effectively deal with load mutation, parameter change and model uncertainty, and greatly enhance the overall stability and performance of Stewart platform. In an application example, the strategy is used in precision machining field to optimize Stewart platform support and accurately control high-speed machine table, facing frequent fluctuation of dynamic load. The fuzzy controller takes displacement error, speed error, cutting force and material hardness as inputs and dynamic stiffness as outputs, and constructs fuzzy rule base and optimized membership function suitable for various machining conditions. The evaluation shows that fuzzy control performs well in transient response, and the response time is shortened by about 30% in the face of large load sudden change. In steady-state accuracy, displacement error ± 0.05 mm and velocity error ±0.1°/s are strictly controlled, which is better than pure PID control. In anti-disturbance test, fuzzy control successfully reduces the influence of random disturbance on platform trajectory by 70%. Robustness tests show that the fuzzy controller maintains stable control effect even when the system parameters vary by ±10%, and the system performance score is above 8.5, which is far superior to that of traditional PID controller under the same conditions.

@article{Zhao2024,
  title     = {Fuzzy Control-based Adaptive Adjustment of Dynamic Stiffness for Stewart Platforms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zhiqiang Zhao and Yuetao Liu and Changsong Yu and Changsong Yu},
  doi       = {10.14569/IJACSA.2024.0150622},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150622}
}