IEEE Transactions on Cybernetics· 2015Q1
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
- 1,260citations
- Q1SCImago
- 2015year
Short summary
A novel adaptive neural network control strategy effectively manages uncertain robotic systems while strictly adhering to full-state constraints, preventing constraint violation and ensuring system stability.
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Key points
- Adaptive neural networks are used to handle uncertainties and disturbances in robotic systems.
- A Moore-Penrose inverse term is incorporated to prevent the violation of full-state constraints.
- A barrier Lyapunov function ensures the uniform ultimate boundedness of the closed-loop system.
- Simulation studies validate the effectiveness of the proposed control strategy.
AI-generated from the title and abstract; the full text is not read.
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 parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.
The authors' abstract, as published at the source. IEEE Transactions on Cybernetics, 2015 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering