Study on Tracking Control of Vascular Interventional Surgical Robot based on Autocoupling PID
Shuxiang Guo, Zefa Sun, Jian Guo
- 发表年份
- 2021
- 引用次数
- 2
摘要
Cardiovascular and cerebrovascular diseases are the biggest killers of human health. With the development of modern science and technology, the use of robotic assisted vascular interventional surgery has become an important means to reduce cardiovascular and cerebrovascular diseases. In the traditional minimally invasive vascular interventional surgery, the doctor is exposed to a large amount of radiation for a long time, which will cause some damage to the doctor. Using surgical robots instead of doctors to operate catheters could overcome these shortcomings. In the operating room can free the doctor from the operation site and prevent radiation damage to the doctor's body. Existing research on robot system is focused on the construction of the system and the development of the related key technologies of the system control research is rarely used more open loop or simple proportional integral differential control scheme, in recent years, there are also studies the combination of PID and intelligent control, the adaptive fuzzy PID, PID neural network, and synovial control, etc., the control method has its own advantages and disadvantages. In order to solve the problem of master-slave control of vascular interventional surgical robot, the traditional PID controller was analysed, and the self-coupling PID controller was designed. In this paper, the simulation results and real experimental results are presented. It is proved that the Autocoupling PID controller can effectively improve the operation ability, effectively solve the problem that the traditional PID is difficult to set, and has a good control effect. It is verified that the Autocoupling PID can be applied to the master-slave control system of the vascular interventional robot.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002