This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties.
This paper investigates the performance of input shaping techniques for sway control of a rotary crane system. Unlike the conventional optimal controllers, input shaping is simple to design and cost effective as it does not require feedback sensors. Several input shapers were implemented and their performances were compared which are useful for future sway control designs. A nonlinear model of the system was derived using the Lagrange’s energy equation. To investigate the performance and robustness of input shaping techniques, zero vibration (ZV), zero vibration derivative (ZVD), zero vibration derivative-derivative (ZVDD) and zero vibration derivative-derivative-derivative (ZVDDD) were proposed with a constant cable dimension. Level of reduction of the payload sway was used to assess the control performance of the shapers. Simulation and real time experimental results have shown that ZVDDD with an average sway reduction of 88% has the highest level of sway reduction and robustness to modeling errors as compared to 85%, 80% and 65% for ZVDD, ZVD and ZV respectively.
This paper presents an output-based command shaping (OCS) technique for an effective payload sway control of a 3D crane with hoisting. A crane is a challenging and time-varying system, as the cable length changes during the operation. The OCS technique is designed based on output signals of an actual system and reference model, does not require the natural frequency and damping ratio of the system, and thus can be utilized to minimize the hoisting effects on the payload sway. The shaper was designed by using the derived non-linear model of a 3D crane. To test the effectiveness of the controller, simulations using a non-linear 3D crane model and experiments on a lab-scale 3D crane were performed and compared with a zero vibration derivative (ZVD) shaper and a ZVD shaper designed using an average travel length (ATL) technique. In both the simulations and the experiments, the OCS technique was shown to be superior in reducing the payload sway with reductions of more than 56% and 33% in both of the transient and residual sways that were achieved when compared with both the ZVD and the ATL shapers, respectively. In addition, the OCS technique provided the fastest time response during the hoisting. It is envisaged that the method can be very useful in reducing the complexity of closed-loop controllers for both tracking and sway control.
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