Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles
- Resource Type
- Conference
- Authors
- Xia, Xiuze; Cheng, Long
- Source
- 2021 13th International Conference on Advanced Computational Intelligence (ICACI) Advanced Computational Intelligence (ICACI), 2021 13th International Conference on. :305-311 May, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Adaptation models
Computational modeling
Force
Predictive models
Muscles
Jitter
Takagi-Sugeno model
Pneumatic artificial muscle
NARMAX model
adaptive T-S fuzzy model
model predictive control
- Language
- ISSN
- 2573-3311
Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM’s characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model’s practicability.