Abstract
In this paper, a novel control method of the redundant force branch based on the force/position hybrid control structure of Smith predictor compensation is proposed. A fuzzy PI controller is designed based on Smith predictor compensation structure and it is included in the redundant force branch. This method can obtain good tracking and dynamic performance. However, fuzzy control doesn't have self-learning and adaptive ability, so fuzzy neural network (FNN) controller is used in the redundant force branch. The simulation results show that the proposed FNN algorithm based on delay compensation force/position hybrid control structure can improve the adaptability and the control accuracy of driving force of redundant branch.
| Original language | English |
|---|---|
| Title of host publication | 2019 WRC Symposium on Advanced Robotics and Automation (WRC SARA) |
| Publisher | IEEE |
| Pages | 142-147 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728155524 |
| ISBN (Print) | 9781728155531 |
| DOIs | |
| Publication status | Published - 16 Dec 2019 |
| Event | 2nd World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2019 - Beijing, China Duration: 21 Aug 2019 → … |
Conference
| Conference | 2nd World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2019 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 21/08/19 → … |
Keywords
- control system synthesis
- delays
- force control
- fuzzy control
- fuzzy neural nets
- mobile robots
- neurocontrollers
- PI control
- position control
- predictive control
- robot dynamics
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Optimization
- Mechanical Engineering