Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance

Abdullahi, Auwalu M.; Mohamed, Zulkifli; Selamat, Hazlina; Pota, H. R.; Abidin, Mastura Shafinaz Zainal; Ismail, Fatimah Sham; Haruna, A.

doi:10.1016/j.ymssp.2017.04.034

Cited by 70 publications

(41 citation statements)

References 30 publications

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“…In both Figures 5 and 6, the displacement and velocity curves of the trolley satisfy the constraint of equation (10), and the trajectory of the trolley is generated as required. The grab swing angle and the swing speed curves are optimized by the HS algorithm according to the fitness function shown in equation (14). Figure 5 is a simulation result when the air resistance and the wind load are ignored.…”

Section: Simulation Results Analysismentioning

confidence: 99%

“…u e and v e in equation (14) are calculated from equation (5). Substituting equations (6) and (8) into equation (4) gives the second-order nonlinear nonhomogeneous differential equation of the grab swing angle u(t) with respect to time, and the differential angle equation is numerically integrated at t 2 ½0, Te to obtain the grab swing angle u(t) and swing speed v(t).…”

Section: Parameter Optimization Of Rbf Neural Networkmentioning

confidence: 99%

“…11,12 Sun et al 13 built the grab and cargo as a double-pendulum model and proposed to use the amplitude-saturated output feedback control method to suppress swing. Abdullahi et al 14 considered the influence of wind on the crane system and proposed an online adaptive output-based command shaping technology to reduce the payload of the crane and successfully reduce the swing. Tuan and Lee 15 introduced the influence of geometric nonlinearity, cable elasticity, and wind disturbance; established a crane model that is more in line with actual working conditions; adopted a sliding mode control for the grab; and analyzed the stability of the system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

Miao

Feng-lin

et al. 2019

Advances in Mechanical Engineering

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The swing of the grab is a main factor affecting the working efficiency of overhead cranes. Thus, planning the optimal motion path can reduce the adverse effects caused by the grab swing and improve the loading and unloading efficiency. The dynamic model of the trolley-grab system is established by considering factors like the change of rope length, wind load, and air resistance. First, the radial basis function neural network is applied to generate a feasible motion trajectory of the crane trolley. Taking the swing angle and angular velocity of the grab at the discharge point as evaluation, the harmony search algorithm is then applied to optimize the neural network parameters and obtain the optimal anti-swing motion trajectory. The numerical simulation and practical testing results show that the harmony search-radial basis function algorithm generates a smooth motion trajectory with good convergence, achieving anti-swing control of the trolley-grab system.

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Section: Simulation Results Analysismentioning

confidence: 99%

Section: Parameter Optimization Of Rbf Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

Miao

Feng-lin

et al. 2019

Advances in Mechanical Engineering

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“…Some researchers have conducted studies on solving the problems of pendulum motion control [38,39]. They propose an adaptive output-based command shaping (AOCS) technique [40], innovative controllers, nonlinear control algorithms, and control schemes [41][42][43] for the vibrations of cranes, but the control technologies that reduce the vibrations rely on the movement of the hanging points. However, the hanging points of many suspended structures are fixed, such as those of ship cranes.…”

Section: Introductionmentioning

confidence: 99%

Robustness of the Active Rotary Inertia Driver System for Structural Swing Vibration Control Subjected to Multi-Type Hazard Excitations

Zhang

Wang

2019

Applied Sciences

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In traditional structural disaster prevention design, the effects of various disasters on structures are usually considered separately, and the effects of multi-type hazards are rarely considered. The traditional Tuned Mass Damper (TMD) and Active Mass Damper/Driver (AMD) are ineffective for the control of swing vibration. The Tuned Rotary Inertia Damper (TRID) system has the problems of being ineffective under multi-type hazard excitation and exhibiting a limited robustness. The Active Rotary Inertia Driver (ARID) system is proposed to solve these problems and the robustness of such an active control system is investigated in this paper. Firstly, the equations of motion corresponding to the in-plane swing vibration of the suspended structure with the ARID/TRID system are established. The control algorithm for the ARID system is designed based on the Linear Quadratic Regulator (LQR) algorithm. Next, numerical analyses carried out using Simulink are presented. Then, numerical analyses and experimental investigations corresponding to five working conditions, i.e., free vibration, forced vibration, sweep excitation, earthquake excitation, and sea wave excitation, are introduced. Lastly, the numerical analyses and experimental results of the ARID system, and numerical results of the TRID system, are compared to demonstrate the effectiveness and robustness of the ARID control system. It can be concluded that the ARID system is effective and feasible in structural swing vibration control and it exhibits a better control robustness than the TRID system. Furthermore, the feasibility of applying the ARID control system to multi-type hazard excitations is validated.

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“…Broadly, the existing control strategies in literature can be categorized into three: open-loop (OL), closed-loop (CL), and hybrid control (HC) schemes. For the OL schemes, the input command shaping (ICS) [2][3][4][5][6][7][8], the filter control [9], the command smoothing techniques (CS) [10], and the trajectory planning (TP) techniques [11][12][13] had been proposed. ICS achieves sway minimization by exciting two or more transient oscillations at different instances that result in effective cancellation of the payload oscillations.…”

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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Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance

Cited by 70 publications

References 30 publications

Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

Robustness of the Active Rotary Inertia Driver System for Structural Swing Vibration Control Subjected to Multi-Type Hazard Excitations

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

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