Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash

Tuấn, Lê Anh; Cuong, Hoang Manh; Trieu, Pham Van; Nho, Luong Cong; Thuan, Vu Duc; Anh, Le Viet

doi:10.1016/j.ymssp.2018.04.030

Cited by 87 publications

(31 citation statements)

References 41 publications

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“…Moreover, the reduction in brake pressure coincides with the positive value of the servo valve spool displacement. Therefore, the dynamic system modeling that involves (1) and (5)(6)(7)(8)(9) is deemed reliable.…”

Section: B Industrial Implementationmentioning

confidence: 99%

“…In a control strategy with a dynamic system (1), (5)(6)(7)(8)(9), an input command signal (voltage) is sent to the servo valve to control the spool movement. Spool displacement determines the level of hydraulic pressure inside a brake chamber and controls the movement of a pneumatic piston.…”

Section: Introductionmentioning

confidence: 99%

“…A single controller developed based on mathematical dynamics also has limited applications. Moreover, the switch part of the dynamic model [30], [31], which is derived from the unbalanced properties of the servo valve circuit (8), (9), is included in the system. Based on the approaches described in [32], [33], this system can be described as a slow switching system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Finite-Time Convergence Control Mechanism for High-Precision Tracking in a Hybrid Fluid Power Actuator

et al. 2020

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In this study, a novel multiphase converged control structure with a switching mechanism is established based on the fundamentals of the fast-terminal sliding mode. This control structure achieves an excellent performance in a nonlinear dynamic system, and its primary objective is to control the piston trajectory in an innovative electrohydraulic, pneumatic, and mechanical hybrid system. This work focuses on abrupt gain-scheduled acceleration, which has been rarely studied in the literature but has increasingly diverse high-technology applications across various industries. The greatest challenge lies in the sensitivity and instability of the system during a sudden actuator acceleration. Moreover, the system parameter uncertainties and external disturbances strongly influence the degree of control of the system. By inheriting the robustness and fast convergence properties of sliding-mode algorithms, the proposed control law not only guarantees a finite-time convergence of tracking errors to their origin but also reduces the impact of composite disturbances in an extremely rapid experimental process. The effectiveness of the proposed structure is analyzed via numerical simulations and industrial implementation.

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Section: B Industrial Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Finite-Time Convergence Control Mechanism for High-Precision Tracking in a Hybrid Fluid Power Actuator

et al. 2020

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“…However, the inclusion of the modern automated quay cranes is still an innovation for smaller ports throughout the world. In the light of the research and progress made in this area, [15][16][17][18][19] many ports in the world lack the application of these innovations.…”

Section: Introductionmentioning

confidence: 99%

Modeling and experimental research of quay crane cargo lowering processes

Eglynas

Andziulis

Bogdevičius

et al. 2019

Advances in Mechanical Engineering

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This article studies an operational problem arising at a container terminal. Klaipeda city port operations were surveilled up close and relevant remarks were made. The time efficiency of the existing container-lowering procedures using the simulation studies with a test-bed and with a real life crane operation was examined. Statistical analysis of the experimental results has showed that non-automated processes have higher time variance for the lowering process. The operations of quay crane for container handling “ship-to-shore” were analyzed, and lowering procedures time variations were determined. Each container is transported at operators own risk and with pre-defined time efficiency; therefore, it is hard to predict the optimal time for each container handling operation, thus, eventually, additional costs arise. Mathematical model was developed, which described dynamical characteristics of the container movement during lowering procedures. The lowering crane operation was modeled using known dynamic values for each separate case, and the complexity of the problem was proven. The results of modeling and experimental results show that it is possible to achieve optimal values with the existing processes.

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“…Chiang et al 7 proposed a sliding mode angle controller with neural network estimator for a fan-plate system, where the neural network estimator is used to estimate the unknown lumped bounded uncertainty of parameter variations and external disturbances. Tuan et al 8 proposed a robust adaptive system for a ship-mounted container crane using second-order sliding mode control and designed a modeling estimator on the basis of RBF network, which approximates almost all the structure of a crane model. Rahmani et al 9 proposed an adaptive neural network integral sliding mode controller to control a biped robot, where the adaptive neural network is applied to estimate the unknown disturbances.…”

Section: Introductionmentioning

confidence: 99%

Adaptive block compensation trajectory tracking control based on LuGre friction model

2019

International Journal of Advanced Robotic Systems

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Aiming at the problems of modeling error and uncertain external disturbance in the multi-joint robot control model, an adaptive block compensation trajectory tracking controller based on LuGre friction model is proposed. Firstly, the algorithm divides the interference term of LuGre friction model into three parts with different physical quantities. Secondly, an adaptive neural network compensator is designed to assess the three parts of the LuGre friction model. Thirdly, a robust sliding mode controller is developed to reduce the influence of these estimation errors of neural network compensator and other uncertain disturbances and ensure that the system converges in a finite time at the same time. Finally, numerical simulations under different input and disturbance signals for the planar multi-joint robot and the inverted pendulum are conducted to validate the effectiveness of the proposed controller, and the performance of the proposed controller is compared with conventional sliding mode controller to illustrate the usefulness and efficiency of the proposed controller.

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Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash

Cited by 87 publications

References 41 publications

Robust Finite-Time Convergence Control Mechanism for High-Precision Tracking in a Hybrid Fluid Power Actuator

Robust Finite-Time Convergence Control Mechanism for High-Precision Tracking in a Hybrid Fluid Power Actuator

Modeling and experimental research of quay crane cargo lowering processes

Adaptive block compensation trajectory tracking control based on LuGre friction model

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