Adaptive hierarchical sliding mode control using neural network for uncertain 2D overhead crane

<pre>The paper proposes a new approach to efficiently control a three-dimensional overhead crane with six degrees of freedom (DoF). In addition to five usual output variables including three positions of the trolley, bridge and pulley and two swing angles of the hoisting cable, it is proposed to consider elasticity of the hoisting cable, which causes oscillation in the cable direction. That is, there exists $6^{th}$ under-actuated output in the crane system. To design an efficient controller for the six-DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes sway and oscillation of the overhead crane when it transports a payload to desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in the synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness.</pre>

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HIGH ORDER SLIDING MODE CONTROL WITH ANTI-SWAY BASED COMPENSATION ON ARTIFICIAL NEURAL NETWORK BY PSO ALGORITHM FOR OVERHEAD CRANE

Vietnam Journal of Science and Technology ◽

10.15625/2525-2518/54/3/8617 ◽

2017 ◽

Vol 55 (3) ◽

Author(s):

Le Xuan Hai ◽

Nguyen Van Thai ◽

Vu Thi Thuy Nga ◽

Hoang Thi Tu Uyen ◽

Nguyen Thanh Long ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sliding Mode Control ◽

Sliding Mode ◽

Pso Algorithm ◽

High Order ◽

Overhead Crane ◽

Mode Control ◽

Artificial Neural

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HIGH ORDER SLIDING MODE CONTROL WITH ANTI-SWAY BASED COMPENSATION ON ARTIFICIAL NEURAL NETWORK BY PSO ALGORITHM FOR OVERHEAD CRANE

Vietnam Journal of Science and Technology ◽

10.15625/0866-708x/54/5/8617 ◽

2017 ◽

Vol 55 (3) ◽

Author(s):

Le Xuan Hai ◽

Nguyen Van Thai ◽

Vu Thi Thuy Nga ◽

Hoang Thi Tu Uyen ◽

Nguyen Thanh Long ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sliding Mode Control ◽

Sliding Mode ◽

Pso Algorithm ◽

High Order ◽

Overhead Crane ◽

Mode Control ◽

Artificial Neural

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An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

10.36227/techrxiv.17704388.v1 ◽

2022 ◽

Author(s):

Linh Nguyen

Keyword(s):

Sliding Mode Control ◽

Degrees Of Freedom ◽

Fuzzy Inference ◽

Sliding Mode ◽

Overhead Crane ◽

Control Approach ◽

Mode Control ◽

Extensive Evaluation ◽

Hierarchical Sliding Mode ◽

Six Dof

<pre>The paper proposes a new approach to efficiently control a three-dimensional overhead crane with six degrees of freedom (DoF). In addition to five usual output variables including three positions of the trolley, bridge and pulley and two swing angles of the hoisting cable, it is proposed to consider elasticity of the hoisting cable, which causes oscillation in the cable direction. That is, there exists $6^{th}$ under-actuated output in the crane system. To design an efficient controller for the six-DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes sway and oscillation of the overhead crane when it transports a payload to desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in the synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness.</pre>

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Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

Advances in Mechanical Engineering ◽

10.1177/1687814018799554 ◽

2018 ◽

Vol 10 (9) ◽

pp. 168781401879955 ◽

Cited By ~ 2

Author(s):

Xiaorong Huang ◽

Hongli Gao ◽

Anca L Ralescu ◽

Haibo Huang

Keyword(s):

Neural Network ◽

Sliding Mode Control ◽

Fuzzy Neural Network ◽

Sliding Mode ◽

Control Law ◽

Network System ◽

Mode Control ◽

Hierarchical Sliding Mode ◽

Fuzzy Neural ◽

Neural Network System

We present an adaptive hierarchical sliding mode control based on fuzzy neural network for a class of underactuated systems to solve the problem of high-precision trajectory tracking. This system is viewed as several subsystems. One subsystem is used to design the first-layer sliding surface, which constructs the second-layer sliding surface with another subsystem. When the top layer includes all the subsystems, the design process is finished. Meanwhile, the equivalent control law and the switching control law are achieved at every layer. Because the hierarchical sliding mode control law relies excessively on the requirement of detailed information of the underactuated dynamic system, and because that method causes an inevitable chattering phenomenon, an online fuzzy neural network system is applied to mimic the hierarchical sliding mode control law. Moreover, the bounds of system uncertainties and modeling error caused by the fuzzy neural network system are estimated online by a robust term. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and Barbalat’s Lemma. Finally, the examples, a single-pendulum-type overhead crane system and an inverted pendulum system, are simulated to verify the effectiveness and robustness of the proposed method compared with some conventional methods.

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