A controller enabling precise positioning and sway reduction in bridge and gantry cranes

Sorensen, Khalid; Singhose, William; Dickerson, Stephen L.

doi:10.1016/j.conengprac.2006.03.005

Cited by 295 publications

(133 citation statements)

References 18 publications

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“…Cranes are among the most popular applications of the input shaping technique due to the requirement to suppress payload swing. Notable literature includes those of gantry crane [3], bridge crane [4], tower crane [5], and boom crane [6]. Robot manipulators have flexibility in their joints and links that have to be taken into account during the design.…”

Section: Introductionmentioning

confidence: 99%

Improving Closed-Loop Signal Shaping of Flexible Systems with Smith Predictor and Quantitative Feedback

Chatlatanagulchai

Poedaeng

Pongpanich

2016

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Abstract. Input shaping is a technique used to move flexible systems from point to point rapidly by suppressing the residual vibration at the destination. The vibration suppression is obtained from the principle of destruction of impulse responses. The input shaper, when placed before the flexible system inside the control loop, proves to deliver several benefits. However, this so-called closed-loop signal shaping has one major disadvantage that it adds time delays to the closed-loop system. Being a transcendental function, the time delays cause difficulty in analysis and design of the feedback controller. In most cases, the time delays also limit the maximum achievable bandwidth. In this paper, for the very first time, Smith predictors were applied to the closed-loop signal shaping to remove the time delay from the loop. It was shown in simulation result that the detrimental effect of the time delays was completely removed in the case of perfect plant model. The quantitative feedback control was used in the study to quantify the amount of achievable bandwidth and to suppress vibrations from the plant-input disturbance.

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Section: Introductionmentioning

confidence: 99%

Improving Closed-Loop Signal Shaping of Flexible Systems with Smith Predictor and Quantitative Feedback

Chatlatanagulchai

Poedaeng

Pongpanich

2016

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“…Motion control for rest-to-rest maneuvers in lightly-damped oscillation systems has been one of the main areas of focus for the development of residual vibration reduction [1][2][3] , and cranes have been one of the widely addressed industrial applications [4][5][6][7][8][9][10][11] . In the case of standard cranes, the cable length and the inertia of the payload will change between maneuvers and, hence, a large variation of the system parameters is foreseen.…”

Section: Introductionmentioning

confidence: 99%

Modified adaptive input shaping for maneuvering cranes using a feedback MEM gyroscope with null drift

Fontanet

Cardona

Català

2015

Int. J. Precis. Eng. Manuf.

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This paper presents an adaptive algorithm to reduce residual vibrations when the feedback sensor used has the drawback of having null drift along the time. The adaptive approaches are useful to deal with large variations of the system parameters at each maneuver, such as it occurs in cranes. For the feedback sensor, the use of inertial measurement units such as Micro-Electro-Mechanical Systems (MEMS) is increasingly extended because of their cost, size, robustness and power consumption. However, the effectiveness of the adaptive input shaping algorithms is compromised because of this drift, which is a commonly raised issue in this kind of devices. For a standard crane application, this major drawback could be avoided with a frequent time-basis calibration of the sensor, but it is not a feasible solution. The study presented in this manuscript focuses on the development of an automatic compensation of this drift to obviate such frequent calibrations. It is based on a non-asymptotic algebraic identification technique, which has the advantage of not requiring initial conditions and having a short convergence time. The new formulation uses the Zero-Vibration (ZV) input shaper technique, and the null drift is added to the algorithm as a new parameter to be identified. The proposed method has been particularized for single maneuvers of cranes with a gyroscope as feedback sensor, in a real time scenario. Experimental results show the efficacy of the method with its application to a scaled crane test platform. P R E -P R I N T V E R S I O N

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“…Singer and Seering in [7] extended the Posicast control by increasing its robustness to parameter uncertainty and patented the technique under the name input shaping. Applications of the input shaping technique range from gantry crane (Sorensen et al in [8]), bridge crane (Peng et al in [9]), tower crane (Huey et al in [10]), boom crane (Huang et al in [11]), flexible-link robot (Chatlatanagulchai et al in [12]), flexible-joint robot (Chatlatanagulchai and Saeheng in [13]), liquid sloshing in container (Baozeng and Lemei in [14]), flexible spacecraft (Zhang and Zhang in [15]), coordinate measuring machine (Singhose et al in [16]), cam follower (Pridgen and Singhose in [17]), telescopic handler (Park and Chang in [18]), cherry picker (Hongxia et al in [19]), dual solenoid actuator (Yu and Chang in [20]), MEMS contact switch (Do et al in [21]), hard disk drive (La-orpacharapan and Pao in [22]), cable-driven crane (Huey and Singhose in [23]), high-rise elevator (Fortgang et al in [24]), wave suppression (Yang and Liang in [25]), atomic force microscopy (Schitter et al in [26]), CNC machine tool (Altintas and Khoshdarregi in [27]), micro-milling machine (Fortgang and Singhose in [28]), wafer stage in chip manufacturing (Roover and Sperling in [29]), automated highway (Bae and Gerdes in [30]), brushless motor (Chang et al in [31]), cooperative motion between two robots (Zhang et al in [32]), twin rotor (Toha and Tokhi in [33]), quadrotor with sling load (...…”

Section: Introductionmentioning

confidence: 99%

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

Chatlatanagulchai¹,

Nithi-Uthai²

2017

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Abstract. Input shaping technique can be used to suppress residual vibration, occurring from moving rapidly a flexible system from one point to another point. An input shaping filter produces a shaped input signal that avoids exciting the flexible modes of the flexible system. The technique requires accurate knowledge of mode parameters. When the plant model is not accurate, performance of the input shaper degrades. Several robust input shapers were proposed to handle this inaccuracy at the expense of longer move time. The purpose of this paper is, for the first time, to present an application of an intelligent backstepping system to matching of the resulting closed-loop system with a reference model. The input shaper can then be designed from the mode parameters of the reference model. Because the reference model is accurate even when the plant model is not, the input shaper needs not be robust, resulting in shorter move time. The intelligent backstepping system consists of a three-layer neural network, a variable structure controller, and a backstepping controller. The neural network is used as a black-box model in case when the plant model is unknown, making the proposed system model-independent. The adaptive property of the neural network also makes the proposed system suitable for nonlinear, time-varying, or configuration-dependent systems. The variable structure controller handles the uncertainty arisen in the system. The backstepping controller, through its virtual controls, provides a means for the control authority to reach the unmatched uncertainty in the system. This study contains simulation and experimental results on a flexible-joint robot manipulator. The results showed that this proposed intelligent input shaping system outperformed previously proposed robust input shapers in terms of allowable uncertainty amount and move time. The proposed system is also relatively easy to apply because it does not require the plant model.

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A controller enabling precise positioning and sway reduction in bridge and gantry cranes

Cited by 295 publications

References 18 publications

Improving Closed-Loop Signal Shaping of Flexible Systems with Smith Predictor and Quantitative Feedback

Improving Closed-Loop Signal Shaping of Flexible Systems with Smith Predictor and Quantitative Feedback

Modified adaptive input shaping for maneuvering cranes using a feedback MEM gyroscope with null drift

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

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