Simmechanics Visualization of Experimental Model Overhead Crane, Its Linearization And Rerence Tracking-Lqr Control

Win, Thein Moe; Hisketch, Tim; Eaton, Ray

doi:10.5121/ijccms.2013.2301

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The gantry crane system depends mainly on three variables namely, the trolley displacement from a reference position ( ), the payload swing angle ( ), and the steel wire elongation ℓ( ). The dynamics of the system is given as follows [5,[15][16][17]:…”

Section: Gantry Crane System Modelmentioning

confidence: 99%

Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements.

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Section: Gantry Crane System Modelmentioning

confidence: 99%

Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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“…For mechanical systems, the overhead crane model is modeled by Win et al (Win et al, 2013) using SimMechanics, and an LQR controller is applied to the system to prevent skidding. Using the experimental model of the system and the mean square root error, the most appropriate mathematical model is presented.…”

Section: Introductionmentioning

confidence: 99%

Study on dynamic modelling and vibration analysis of single-link flexible manipulator

Uyar

2022

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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In this study, SimMechanics-based dynamic modeling and vibration analysis of a single-link flexible manipulator are studied. A flexible beam model is set up for modeling the manipulator. Transient analysis is performed to obtain the endpoint acceleration vibration responses of the manipulator moved with different motion profiles. Both the experimental and simulation acceleration results are presented for different cases. The root means square (RMS) values of the acceleration responses are calculated to evaluate the compatibility performance in verifying the simulation results with the experimental results. It has been observed that the results match each other successfully for different cases. The reliability of the SimMechanics-based dynamic modeling method of manipulators is discussed with the experimental results.

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“…In order to overcome the influence of the external disturbance, various closed loop methods are proposed, which can be divided into adaptive control [6][7][8], fuzzy logic-based control [9,10], genetic algorithms (GA) [11,12], feedback linearization control [13,14], linear quadratic regulator (LQR) control [15][16][17], proportional-integral-differential (PID) control [18][19][20], etc. In [21], Zhang and He considered the crane velocity, the position error, and the swing angle totally in a linearized manner, and an adaptive sliding mode controller is designed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Motion Control Optimization for the Bridge Crane System

et al. 2018

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Abstract:A novel control algorithm combining the linear quadratic regulator (LQR) control and trajectory planning (TP) is proposed for the control of an underactuated crane system, targeting position adjustment and swing suppression. The TP is employed to control the swing angle within certain constraints, and the LQR is applied to achieve anti-disturbance. In order to improve the accuracy of the position control, a differential-integral control loop is applied. The weighted LQR matrices representing priorities of the state variables for the bridge crane motion are searched by the multi-objective genetic algorithm (MOGA). The stability proof is provided in order to validate the effectiveness of the proposed algorithm. Numerous simulation and experimental validations justify the feasibility of the proposed method.

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Simmechanics Visualization of Experimental Model Overhead Crane, Its Linearization And Rerence Tracking-Lqr Control

Abstract: ABSTRACT

Cited by 4 publications

References 4 publications

Controller design for gantry crane system using modified sine cosine optimization algorithm

Controller design for gantry crane system using modified sine cosine optimization algorithm

Study on dynamic modelling and vibration analysis of single-link flexible manipulator

Multi-Objective Motion Control Optimization for the Bridge Crane System

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