Container Crane Controller with the Use of a NeuroFuzzy Network

Ferreira, Ricardo; Martiniano, Andréa; Ferreira, André; Romero, Marcio; Sassi, Renato José

doi:10.1007/978-3-319-51133-7_15

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…In order to eliminate oscillations of the load, [9] have proposed position control with a fuzzy logic corrector to control the position of the load and the length of the wire in parallel, Adaptive control has also been addressed in this framework. You can see here also: Fuzzy logic are widely used recently in control of crane and its model [10]- [14].Also Neuronal network and neuro-fuzzy gain place in the control techniques [15]- [20].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of industrial gantry crane model "3D Crane"

2022

IJETER

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3D Crane represent Laboratory model of industrial gantry cranes which are widely used in industry. In this work, a dynamic model of 3D Crane is presented and tow artificial intelligence techniques were synthetized and applied to the model : Fuzzy Logic Control (FLC) and Adaptive Neural Fuzzy Inference System (ANFIS); the purpose of the control is the minimization of swing angles with continued reference trajectory in the three axes. A robustness test against external disturbance of the implemented controls was also apply. The results of the applied techniques are discussed.

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Section: Introductionmentioning

confidence: 99%

Intelligent Control of industrial gantry crane model "3D Crane"

2022

IJETER

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“…The container crane transports the loads to the desired position as quickly and as accurately as possible without colliding with any other equipment. In recent years, with the rapid increase of world trade as well as the need for larger container ships, shipping companies have resorted to an increase of the vessel size [53]. It is essential to be able to load and unload even in adverse weather conditions such as strong winds.…”

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confidence: 99%

How to Apply Fuzzy MISO PID in the Industry? An Empirical Study Case on Simulation of Crane Relocating Containers

et al. 2020

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The proportional-integral-derivative (PID) algorithm automatically adjusts the control output based on the difference between a set point and a measured process variable. The classical approach is broadly used in the majority of control systems. However, in complex problems, this approach is not efficient, especially when the exact mathematical formula is difficult to specify. Besides, it was already proven that highly nonlinear situations are also significantly limiting the usage of the PID algorithm, in contrast to the fuzzy algorithms, which often work correctly under such conditions. In the case of multidimensional objects, where many independently operating PID algorithms are currently used, it is worth considering the use of one fuzzy algorithm with many-input single-output (MISO) or many-input many-output (MIMO) structure. In this work, a MISO type chip is investigated in the study case on simulation of crane relocating container with the external distribution. It is an example of control objects that due to badly conditioned dynamic features (strong non-linearities) require the operator’s intervention in manual or semi-automatic mode. The possibility of fuzzy algorithm synthesis is analyzed with two linguistic variable inputs (distance from −100 to 500 mm and angle from −45° to 45°). The output signal is the speed which is modelled as a linguistic power variable (in the domain from −100% to 100%). Based on 36 fuzzy rules, we present the main contribution, the control system with external disturbance, to show the effectiveness of the identified fuzzy PID approach with different gain values. The fuzzy control system and PID control are implemented and compared concerning the time taken for the container to reach the set point. The results show that fuzzy MISO PID is more effective than the classical one because fuzzy set theory helps to deal with the environmental uncertainty. The container’s angle deviations are taken into consideration, as mitigating them and simultaneously maintaining the fastest speed possible is an essential factor of this challenge.

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Container Crane Controller with the Use of a NeuroFuzzy Network

Cited by 2 publications

References 15 publications

Intelligent Control of industrial gantry crane model "3D Crane"

Intelligent Control of industrial gantry crane model "3D Crane"

How to Apply Fuzzy MISO PID in the Industry? An Empirical Study Case on Simulation of Crane Relocating Containers

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