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

Miao, Yongwu; Feng-lin, XU; Hu, Yanwei; An, Jianping; Zhang, Ming

doi:10.1177/1687814019834458

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…Comparing the results of experimental group 1 (Figures [12][13][14], in the non-zero initial state, the three control methods have varying degrees of overshoot, and the maximum control amount also increases. However, all three control methods cause the system to converge to a stable state within 8 s. Further analysis shows that the trolley convergence speed (ATSMC: 4.8 s, MSMC: 7.3 s, LQR: 5 s) and overshoot (ATSMC: 0.04 m, MSMC: 0.09 m, LQR: 0.08 m) are smaller.…”

Section: Discussionmentioning

confidence: 98%

“…A better control effect is obtained, even in the case of external interference. Therefore, a series of closed-loop control methods have been proposed, including model predictive control, 6 adaptive control, 7,8 output-feedback control, 9,10 intelligent control, [11][12][13][14] energy-based control [15][16][17][18] and sliding mode control. [19][20][21][22][23][24][25][26][27][28][29] The design of bridge crane control systems often involves the problems of model uncertainty, parameter perturbation and external interference in practical industrial applications, which can be solved by the sliding mode control efficiently.…”

Section: Introductionmentioning

confidence: 99%

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A novel model-free adaptive terminal sliding mode controller for bridge cranes

Wang

Tan

Qiu

et al. 2023

Measurement and Control

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To achieve stabilisation control of an underactuated bridge crane system, a new robust control strategy for the sliding mode is proposed in this paper. It can realise finite-time-convergent stabilisation control under the conditions of model uncertainty, parameter perturbation and external interference. In contrast to the existing methods, our method does not need prior information of the dynamic characteristics of the bridge crane system, and can make the system converge to the equilibrium state at the preset time. Specifically, the nonlinear model of the bridge crane system is linearised with partial feedback, and adaptive signals are introduced. Then, according to the form of the transformed system, a fast terminal sliding mode surface is constructed, and an adaptive terminal sliding mode controller is designed. According to strict analysis, the proposed control law ensures that the system converges to the equilibrium point in finite time and provides the convergence time. Finally, the effectiveness and robustness of the proposed control method are verified by comparing the simulation and experimental results with existing methods.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A novel model-free adaptive terminal sliding mode controller for bridge cranes

Wang

Tan

Qiu

et al. 2023

Measurement and Control

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“…Modern approaches belong to a group of methods that synthesize a certain movement of the suspension point, which, in turn, optimizes the payload movement [13]. It involves using the shaping controllers [14], the mathematical apparatus of fuzzy logic [15], the control by means of neural networks [16], the inverse dynamics method [17] and other approaches [18]. In paper [19], a method is proposed and a mathematical model is developed to solve the problem of moving a payload by a bridge crane on a non-rigid rope suspension along any curved horizontal trajectory specified as a smoothed curve.…”

Section: Introductionmentioning

confidence: 99%

The study of the deviations of the payload moved along the curved trajectory by the crawler crane

Корытов

Shcherbakov

Titenko

et al. 2022

J. Phys.: Conf. Ser.

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Reducing the uncontrolled deviations of the payload moved by a crawler crane with a constant boom length is a relevant objective, since it improves the operational efficiency and safety, as well as the positional accuracy of the payload at the target point. In this regard, the results of the research aimed at developing a trajectory synthesis method of two controlled coordinates of the crane, such as the swing angles of the rotary platform and boom raising which ensure a predefined trajectory of the moving payload, have been described. Furthermore, the influence of the payload movement parameters, such as the time of the prescribed payload displacement, the initial angle of the boom lift, the hoist rope length, the overall dimensions of the required payload displacement trajectory on the error of the predefined payload trajectory execution by the lifting crane has been studied. For example, we examined a predefined arc curved trajectory of the moving payload. The horizontal coordinates of the trajectory of the upper suspension point located on the boom head and providing the predefined payload trajectory were generated using the Matlab mathematical simulation model developed in the Simulink package for solving a linearized differential equation. This equation describes the deviations of a pendulum suspended payload with a horizontally moving suspension point in a separate vertically located plane of the space. The superposition of two horizontal trajectories obtained by solving the given equation ensures the payload movement along a curved trajectory predefined in the horizontal plane. The trajectory of the crane’s controlled coordinates was calculated from the horizontal coordinates of the suspension point. When executing the trajectory, the examined additional errors occur. It was revealed that when the time of moving along a predefined payload trajectory exceeds 40 seconds, the residual oscillations become insignificant and payload positional accuracy at the target point increases. It suggests the limited applicability of the developed method when implemented on the rotary lifting cranes with a constant boom length. The range of the local minima errors of the predefined trajectory implementation depending on the initial inclination angle of the boom, which varies in the average values range of the boom inclination angle, has been defined.

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“…The control system was designed based on the adaptive feedback control and predictive unity magnitude shaper system using the NNUMZV-APIDLNN algorithm. Moreover, some researchers present the study of the Fuzzy and LQR-PID controllers for anti-swing control of overhead cranes [18], the Harmony Search radial basis function neural network algorithm for optimal anti-swing motion trajectory [19], the differential evolution (DE) algorithm based fuzzy logic for PID optimal parameters [20], the PSO algorithm for PID-PD parameters optimization [21], and the Riccati discrete-time transfer matrix method of a multibody system (MS-RDTTMM) for control design of overhead motion [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

2021

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Different industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

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

Cited by 11 publications

References 15 publications

A novel model-free adaptive terminal sliding mode controller for bridge cranes

A novel model-free adaptive terminal sliding mode controller for bridge cranes

The study of the deviations of the payload moved along the curved trajectory by the crawler crane

Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

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