Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
University of Science and Technology Beijing · University of Electronic Science and Technology of China
Abstract
This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is…
Citation impact
- FWCI
- 69.17
- Percentile
- 100%
- References
- 67
Authors
2Topics & keywords
- Control theory (sociology)
- Stability (learning theory)
- Artificial neural network
- Robot
- Computer science
- Impedance control
- Lyapunov function
- Lyapunov stability