Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

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...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 29; H. 4; S. 1174 - 1186
Hauptverfasser: He, Wei, Dong, Yiting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.04.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive control
Adaptive systems
Artificial neural networks
Computer simulation
constraint
Constraint modelling
Control stability
Force
Fuzzy control
Fuzzy logic
fuzzy logic control
Impedance
impedance learning
Learning
Liapunov functions
Machine learning
Network control
Neural networks
neural networks (NN)
robot
Robots
Stability analysis
System dynamics
Uncertainty
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung: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 used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov's stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2017.2665581