A study on gantry crane control using neural network two degree of PID controller

Choi, Sung Uk; Kim, J.H.; Lee, J.W.; Lee, Y.J.; Lee, K.S.

doi:10.1109/isie.2001.932001

Cited by 12 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and variation in drive motor speed may cause problems like imprecise trolley locating and great amplitude of load swing [6,7]. The anti-swing control limits swing continuously by altering the speed command signal sent to the converter, as shown in Fig.…”

Section: Problems and Limitations In Connection With Locating Precisimentioning

confidence: 99%

Research on unattended hoisting in solid waste disposal

Chen

et al. 2016

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

Section: Problems and Limitations In Connection With Locating Precisimentioning

confidence: 99%

Research on unattended hoisting in solid waste disposal

Chen

et al. 2016

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

“…Choi et al. [18] and Suh et al. [19] used offline pre‐trained NN self‐tuner to tune PID parameters online.…”

Section: Introductionmentioning

confidence: 99%

A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

Chen

Gao

et al. 2019

Asian Journal of Control

View full text Add to dashboard Cite

Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti-sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti-sway controlling. KEYWORDS adaptive control, anti-sway control, double-pendulum crane systems, Lyapunov stability theory, neural network control 1 | INTRODUCTION Crane systems with cable hoisting mechanism are typically under-actuated systems. They play an important role in logistics and are widely used in container harbors, railway container yards, and other industrial factories. As cable hoisting mechanism is adopted, the weight of the crane is reduced, but the payload swings unavoidably during the transportation as well as reaching the desired position. The sway of the payload decreases the efficiency of crane's transportation and may result in hazards, such as collision, tip over, and etc. To improve the effectiveness and the safety of crane, researchers proposed many anti-sway control strategies to position the trolley accurately and eliminate the payload sway. All the anti-sway control strategies aim to: i) move the trolley safely, fast, and accurately to the desired position, and ii) decrease the sway of the payload as much as possible during the transportation and eliminate the residual sway when reaching the desired position. TIf the parameters of crane systems are exactly known, open-loop control strategies can work effectively. Singhose et al. reviewed the command shaping/input shaping [1] and implemented the method on doublependulum crane systems [2,3]. It shows that the method

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

A study on gantry crane control using neural network two degree of PID controller

Cited by 12 publications

References 6 publications

Research on unattended hoisting in solid waste disposal

Research on unattended hoisting in solid waste disposal

A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

Multi-Objective Motion Control Optimization for the Bridge Crane System

Contact Info

Product

Resources

About