Multilayer neural network based asymptotic motion control of saturated uncertain robotic manipulators

摘要

Composite influences coming from signal measurement noises, unknown nonlinear dynamics, external disturbances and input saturation nonlinearity make it challenging to synthesize high-performance closed-loop control algorithms for uncertain robotic manipulators. In the face of these challenges, we employ the nonlinear multilayer neural networks to approach uncertain nonlinear dynamics and exploit the robust adaptive control to deal with external disturbances without knowing their bounds in advance. More importantly, robust adaptive based auxiliary functions are creatively introduced to offset the possible input saturation nonlinearity. Furthermore, the desired trajectory based model compensation technology is integrated into the control scheme to reduce measurement noises as much as possible. In theory, the global closed-loop stability of the dynamical uncertain system is testified and significant asymptotic tracking result can be acquired. The application verification under different working conditions including severe high-frequency working conditions is implemented to indicate the high-performance effect of the synthesized intelligent controller.