Stable neural PID anti-swing control for an overhead crane

PD with compensation or PID are the most popular algorithms for the overhead crane control. To minimize steady-state error with respect to uncertaintie, 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 theory contributions of this paper are semiglobal asymptotic stability of the neural PID for the anti-swing control is proven with standard weights training algorithms. The conditions give explicit selection methods for the gains of the linear PID control. A experimental study on an overhead crane with this neural PID control is addressed.

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