Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
University of Science and Technology Beijing · University of Electronic Science and Technology of China
Abstract
This paper studies the tracking control problem for an uncertain n -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design…
Citation impact
- FWCI
- 150.97
- Percentile
- 100%
- References
- 55
Authors
3Topics & keywords
- Control theory (sociology)
- Adaptive control
- Lyapunov function
- Control engineering
- Artificial neural network
- Computer science
- Robot manipulator
- State (computer science)
- Peace, Justice and strong institutions