Stable Neural Pid Anti-Swing Control For An Overhead Crane

Abstract

PD with compensation or PID is the most popular algorithms for the overhead crane control. To minimize steady-state error with respect to uncertainties, PID control needs a big integral gain and the PD with compensator requires a large derivative gain. Both of them deteriorate transient performances of the crane control. In this paper, we propose a novel anti-swing control strategy which combines PID control with neural compensation. The main theoretical contributions of this paper are semi global asymptotic stability of the neural PID for the anti-swing control and local asymptotic stability of the neural PID control with a velocity observer are proven with standard weights training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an overhead crane with this neural PID control is addressed.