Adaptive Neural Control for Euler-Lagrange Systems with Unknown Control Direction
- Resource Type
- Conference
- Authors
- Meng, Fanfeng; Zhao, Lin; Yu, Jinpeng
- Source
- 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Data Driven Control and Learning Systems Conference (DDCLS), 2019 IEEE 8th. :1102-1105 May, 2019
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Adaptive systems
Control systems
Manipulators
Neural networks
Adaptation models
Backstepping
Adaptive Neural Control
Command Filtered Backstepping
Euler-Lagrange Systems
Unknown Control Direction
- Language
In this paper, the adaptive neural network control is proposed for Euler-Lagrange systems with unknown control direction. The command filters based virtual control signal, adaptive update law and error compensation mechanism are designed respectively under the Nussbaum type function, which can guarantee that the joint position tracking error reaches to a desired region when the control direction is unknown. The used finite-time command filter can guarantee that the output of the command filter fast approximate the differential of virtual signal.