PD with neuro-adaptive compensation control using the signed power function

Abstract

This paper presents a neuro-adaptive control scheme dedicated to solve the motion trajectory tracking problem of robot manipulators under uncertain parameters and external disturbances. A two degrees of freedom direct-drive robot manipulator was taken as a case of study. The proposed controller is able to guarantee asymptotic convergence of the position and velocity tracking errors, and the weights of the artificial neural network are bounded. Artificial neural network weights are updated online using filtered error approach, adaptive laws and signed power function. This scheme does not require any offline training. The neuro-adaptive controller is experimentally validated and compared with a classical one-layer neuro-adaptive controller; the proposed scheme obtains better quantitative metrics related with the RMS values of the position and velocity errors, and a good robust behaviour against external disturbances.

Keywords:

• Adaptive neural networks
• dynamic neural networks
• real-time experiments
• robot manipulator
• intelligent feedback control

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes

1 The derivative of $|x{|}^n$ is $n|x{|}^{n-1}\mathrm{sign}(x)\dot{x}$

2 $|r{|}^{\alpha }\mathrm{sign}(r)r=|r{|}^{\alpha }\frac{|r|}{r}r=|r{|}^{\alpha +1}$

Additional information

Funding

This work was supported by CONACyT (Consejo Nacional de Ciencia y Tecnología) Projects and TecNM Projects (Tecnológico Nacional de México), México.