Anti-Swing Control of an Overhead Crane by Using Genetic Algorithm Based LQR

Önen, Ümit

doi:10.18535/ijecs/v6i6.12

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…To improve the PID parameters in the gantry crane system, meta-heuristic approaches are used. To regulate the position and sway of an overhead crane, the LQR controller's settings are optimized using a genetic algorithm [5]. A combination of PID and fuzzy control creates a stable overhead crane controller [6].…”

Section: Introductionmentioning

confidence: 99%

Anti-Swing Rejection Based on PID Controller Optimized by Firefly Algorithm

Saleh¹,

Al-khafaji²

2023

MMEP

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Gantry crane systems are often used in a variety of industrial applications to move and raise enormous weights. The process of transferring loads and weights in crane systems has always occupied the interest of researchers because of its importance in maintaining the safety of workers on the one hand and preserving the load itself from damage as a result of its swing and the possibility of it colliding with surrounding objects on the other hand. This paper's primary goal is to use a PID controller to regulate the position and swing of a GCS utilizing genetic algorithms (GA) and firefly algorithms (FA). Analytical techniques are used in the mathematical model, while Simulink in MATLAB is used in the PID model. This technique gave excellent results compared to either using the genetic algorithms (GA).

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Section: Introductionmentioning

confidence: 99%

Anti-Swing Rejection Based on PID Controller Optimized by Firefly Algorithm

Saleh¹,

Al-khafaji²

2023

MMEP

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“…Other methods that have been applied to modeling material handling systems include Takagi–Sugeno fuzzy models [ 4 , 5 ], bond graph methods [ 6 ], multi-body dynamics [ 1 , 7 ] and neural networks [ 8 ]. Evolutionary algorithms have been implemented in a variety of crane applications including anti-sway crane control [ 9 ], scheduling [ 10 , 11 ] and proactive maintenance [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

Kusznir

Smoczek

2022

Sensors

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This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method.

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“…Genetic programming, which was developed by Koza [11] and is related to genetic algorithms, has been used in input-output identification of nonlinear dynamical systems [4,9,15] without having selected a particular model structure a priori. There have been several works [1,2,3,10,17] that have utilized genetic algorithms for the control, identification and planning of cranes. In contrast to other machine learning methods such as artificial neural networks, which have been able to fit models to high accuracy, but as black box models lose their interpretability [5], genetic programming is capable of symbolic regression in which an analytical relationship of the model is obtained.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Based Identification of an Overhead Crane

Kusznir

Smoczek

2021

Journal of KONBiN

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Overhead cranes carry out an important function in the transportation of loads in industry. The ability to transport a payload quickly and accurately without excessive oscillations could reduce the chance of accidents as well as increase productivity. Accurate modelling of the crane system dynamics reduces the plant-model mismatch which could improve the performance of model-based controllers. In this work the simulation model to be identified is developed using the Euler-Lagrange method with friction. A 5-step ahead predictor, as well as a 10-step ahead predictor, are obtained using multi-gene genetic programming (MGGP) using input-output data. The weights of the genes are obtained by using least squares. The results of 15 different genetic programming runs are plotted on a complexity-mean square error graph with the Pareto optimal solutions shown.

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Anti-Swing Control of an Overhead Crane by Using Genetic Algorithm Based LQR

Cited by 7 publications

References 23 publications

Anti-Swing Rejection Based on PID Controller Optimized by Firefly Algorithm

Anti-Swing Rejection Based on PID Controller Optimized by Firefly Algorithm

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

Genetic Programming Based Identification of an Overhead Crane

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