Neuro-fuzzy self-tuning of PID control for semiglobal exponential tracking of robot arms

摘要

The PID controller with constant feedback gains has withstood as the preferred choice for control of linear plants or linearized plants, and under certain conditions for non-linear ones, where the control of robotic arms excels, In this paper a model-free self-tuning PID controller is proposed for tracking tasks. The key idea is to exploit the passivity-based formulation for robotic arms in order to shape the damping injection to enforce dissipativity and to guarantee semiglobal exponential convergence in the sense of Lyapunov. It is shown that a neuro-fuzzy network can be used to tune dissipation rate gain through a self-tuning policy of a single gain. Experimental studies are presented to confirm the viability of the proposed approach.