Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique

Jaafar, Hazriq Izzuan; Sulaima, Mohamad Fani; Mohamed, Zulkifli; Jamian, Jasrul Jamani

doi:10.1109/csudet.2013.6670992

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…However, the Proportional-Integral-Derivative (PID) controller is still the main choice for the industry. Unlike other controllers, PID has a simple structure, fast design process and robust performance in a wide range of operating conditions [4]. In the past study, a self-tuning fuzzy PID controller has been proposed in 2 Zheng et al, [5] for a volume control the electro-hydraulic press.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of EHA System using Weight Aggregation Strategy in MOPSO-PID

Chai¹,

Ghazali²,

Horng³

et al. 2020

ARAM

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In this paper, an improved Particle Swarm Optimization (PSO) that combines the Proportional-Integral-Derivative (PID) controller for positioning control in Electro-Hydraulic Actuator (EHA) system is proposed. Conventionally, PSO with single objective function is designed to determine the optimal parameters of the controller. However, PSO with single objective function might not solve the problem effectively. Hence, a Multi-Objective PSO (MOPSO) is proposed in this work. Two objective functions will be used in the optimization task, which includes mean error and overshoot percentage. The most popular method in MOPSO is Linear Weight Summation (LWS). This paper focuses on investigating the effect of different weight factors combination between mean error and overshoot. Time domain analysis such as overshoot percentage, steady-state error, and mean error will be used to analyse the positioning performance of the EHA system. The results showed that the EHA system performed better by using the best combination of weight factors between mean error and overshoot.

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Section: Introductionmentioning

confidence: 99%

“…Another tuning method is Ziegler-Nichols, which is one of the popular and simple methods. Unfortunately, this method is shown to be aggressive, and it will lead to large overshoot and oscillatory response [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of EHA System using Weight Aggregation Strategy in MOPSO-PID

Chai¹,

Ghazali²,

Horng³

et al. 2020

ARAM

View full text Add to dashboard Cite

show abstract

“…Several CL solutions have been developed for control of cranes. The proportional-integral control (PID) was proposed by [15][16][17] and positive results were obtained. For the PID techniques, the authors used a PID controller for position control and a PD controller for sway suppression.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations

Shehu

Huang

et al. 2019

Journal of Control Science and Engineering

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The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold’s slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm.

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“…19 Moreover, the loading process requires a fast container transport to the required position, but the transport speed is as high as possible, thus the load varies more, thereby aggravating the operation of the operator and the entire transportation process. 20,21 Freight fluctuations also affect the operation of an experienced operator and reduce the positioning accuracy of the spreader. In order to achieve a higher positioning accuracy of the crane spreader, it is necessary to install a control system that evaluates the trolley acceleration and the amplitude of swinging.…”

mentioning

confidence: 99%

“…Traditional parameter-matching techniques, such as the test and error method, are one of the easiest ways to reconcile the PID controller, but the results obtained by this method do not guarantee significant and effective results. 18,21,41 Another method for controlling the controller is Ziegler-Nichols, 20 which is most widely used because of its simplicity. Depending on the results obtained due to the aggressiveness and excessive fluctuations in the variations, researchers have used other methods to reconcile the parameters of the PID controller.…”

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confidence: 99%

Analysis of the efficiency of shipping containers handling/loading control methods and procedures

Eglynas

Jusis

Jakovlev

et al. 2019

Advances in Mechanical Engineering

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Most modern quay cranes operate under the operator's control. Lifting, lowering, and transporting a container from one platform to another are just some of the actions that a person is responsible for, but the negative consequences of handling can be caused not only by his actions. An error, loading transient instability, or an undervalued environmental factor in the control algorithm can cause a risk to human safety, container, and cargo security. In order to control cargohandling risk, it is necessary to improve the cargo control systems not only by changing their software, but also by creating additional control algorithms and systems. These systems with programmed control algorithms should be integrated into existing systems to control cargo security and its transfer time. In this article, transient processes and dynamic property of the cargo-handling operation are described and multibody dynamics simulation performed using laboratory prototype of a quay crane. The experimental research performed and integrated autonomous quay crane control algorithm developed with the proposed embedded container swinging control subroutine operated in optimal mode when the control system used PID controller with a feedback including additional PI controller and S-shaped input signal for the analyzed case with the defined parameter set.

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Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique

Cited by 12 publications

References 18 publications

Performance Evaluation of EHA System using Weight Aggregation Strategy in MOPSO-PID

Performance Evaluation of EHA System using Weight Aggregation Strategy in MOPSO-PID

Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations

Analysis of the efficiency of shipping containers handling/loading control methods and procedures

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