Abstract
The paper focuses on comparing the model predictive control, feedback controller and input shaping control for a laboratory scaled overhead traveling crane. The adaptive control of an overhead crane is developed based on the Generalized Predictive Control (GPC) procedure. Particle swarm optimization is applied to solve the optimal constrained control problem. The recursive least square (RLS) algorithm is applied to real-time estimate parameters of discrete-time model of a crane dynamic system. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory stand. The results of experiments are presented and compared with the performances of the feedback controller and zero vibration derivative (ZVD) input shaper.
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