Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

Zhang, Menghua; Ma, Xin; Song, Rui; Rong, Xuewen; Tian, Guohui; Tian, Xiaohui; Li, Yibin

doi:10.1109/jas.2018.7511072

Cited by 68 publications

(35 citation statements)

References 42 publications

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“…Also, considering (31), the following can be obtained: Similarly, considering (32), it can also be verified that…”

Section: Neural-network Design As Shown Inmentioning

confidence: 79%

“…The model predictive control (MPC) was proposed by Wu et al [21]. The SMC control (which is the focus of this work) had been studied in [1,[22][23][24][25][26][27][28][29][30][31][32]. SMC is a special type of variable structure control characterized by a discontinuous control structure that switches as the system crosses certain manifold in the statespace to drive the system trajectory to reach and subsequently remain on the sliding manifold within the state space [33].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Also, considering (31), the following can be obtained: Similarly, considering (32), it can also be verified that…”

Section: Neural-network Design As Shown Inmentioning

confidence: 79%

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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“…However, it requires a certain knowledge of system parameters discussed in the above mentioned specialized literatures, while in many practices it is difficult to obtain accurately (Kara & Mary, 2017, Yue et al, 2016. In (Zhang et al, 2018), the APD-SMC method was employed to enhance the capability of 2D overhead crane systems as it is simple and robust. A robust model-free inner-outer layer controller scheme was proposed in (Gao et al, 2019), which had the advantages of simplicity and robustness.…”

Section: Introductionmentioning

confidence: 99%

Robust Coordinated Tracking Control of Multiple Robots System Under Bounded Inputs

Gao¹,

Song²,

Zheng³

et al. 2020

STUD INFORM CONTROL

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The coordinated tracking control of multiple robots system with input saturation and obstacle avoidance is investigated in this paper. Additionally, a robust hierarchical outer-inner layer controller is proposed. In order to avoid obstacles and realize coordination tracking control, null-space-based behavioural (NSB) control is applied in the outer layer where it generates the velocity required by the inner layer. An improved adaptive radical basis function neural networks (RBFNNS) proportional derivative-sliding mode control (IRPD-SMC) method is proposed in the inner layer to achieve robust tracking, null steady-state errors, and bounded inputs. Finally, in an environment with obstacles, all robots can track the target at a fixed distance and distribute around it evenly. The convergence and stability of the system are certified by Lyapunov stability theory. Simulation results in the 2D and 3D space show that the proposed IRPD-SMC is effective, by comparing the performances of this method with those of proportional derivative-sliding mode control (PD-SMC), adaptive proportional derivative-sliding mode control (APD-SMC), and adaptive sliding mode control (ASMC).

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“…The task of the robot crane is to find a path free of collisions starting position moves to the target position in a environment with obstacles [17]. To increase productivity, the overhead crane transports the payload as fast as possible to its destination [18]. Basically, the crane drive system uses a motor.…”

Section: Introductionmentioning

confidence: 99%

A Crane Robot of Three Axes Dimensional Using Trajectory Planning Method

Pangaribowo

Hajar

Andika

et al. 2020

International Journal of Advanced Technology in Mechanical, Mec

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This study aims to design a crane robot that has good performance with good stability, good accuracy in the gripper clamping the object at the point of balance and reach the target location well. The crane controller is installed with US-100 ping sensor and proximity infrared sensor to detect position of object. The robot crane moves on the x , y and z axes or in three dimensions using motors as actuator and it can be adjusted with motor drive. The crane moves on the x and y axes using DC motor and z axis using servo motor. The crane automatically moves when it detects an object. The crane's movement uses the trajectory determination method by maintaining speed. Finally, the average accuracy of the gripper clamping exactly at the midpoint of the object is 93%. The length of the object when it is clamped has an accuracy of 95%. The performance of the crane robot is evaluated to transfer an object to the destination location takes 11 seconds with a track length of 86.055 cm

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Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

Cited by 68 publications

References 42 publications

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

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

Robust Coordinated Tracking Control of Multiple Robots System Under Bounded Inputs

A Crane Robot of Three Axes Dimensional Using Trajectory Planning Method

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