Parallel neural network combined with sliding mode control in overhead crane control system

Lee, Lun-Hui; Huang, Pei-Hsiang; Shih, Yi-Pei; Chiang, Tung-Chien; Chang, Cheng-Yuan

doi:10.1177/1077546312464681

Cited by 61 publications

(24 citation statements)

References 11 publications

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“…Due to their inability to cope with perturbations and disturbances, some researchers combined them with closed‐loop controllers to improve the control performance . As for closed‐loop control schemes, it can be further categorized into proportional integral derivative, linear quadratic regulator, model predictive control (MPC), adaptive control, sliding mode control, etc. The closed‐loop control schemes listed above are all model‐based methods, which suggests that their control performances severely depend on the accurate mathematical modeling of overhead crane systems.…”

Section: Introductionmentioning

confidence: 99%

“…The closed-loop control schemes listed above are all model-based methods, which suggests that their control performances severely depend on the accurate mathematical modeling of overhead crane systems. When accurate system dynamics cannot be obtained, intelligent control methods, such as neural network, 15,27,30 fuzzy logic control, 18,28,31,32 and genetic algorithm, 33 can be used.Some closed-loop control methods first generate a reference trajectory and design the online controller to track the reference trajectory. 12,14 Generally speaking, it is difficult to achieve fast transportation and efficient suppressing of swing angles by tracking the reference trajectory.…”

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confidence: 99%

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confidence: 99%

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A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

Wang

Liu

Zhang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

Wang

Liu

Zhang

et al. 2019

Intl J Robust & Nonlinear

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“…Sun et al [28] designed an adaptive control scheme to deal with the control problem of tower crane systems with parametric uncertainties without approximating the non-linear dynamics. There are also some intelligent control methods applied in crane systems such as fuzzy control [29,30], genetic algorithm [31], and neural network [32]. According to the operating experience of real cranes, it is also essential to design suitable trajectories for the system states (positions, velocities, and accelerations).…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, Sun et al [10,11] present antiswing controllers to regulate the cargo position to the desired location asymptotically in the presence of ship roll and heave movements for offshore crane systems applied in modern ocean transportation and logistics. Moreover, existing methods also include input shaping [12][13][14][15], feedback control [16][17][18][19][20][21][22][23][24][25][26][27][28], intelligent control [29][30][31][32], and trajectory planning method [33][34][35][36]. Specifically, several input shapers are designed to reduce payload swing of bridge crane systems [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Robust Control of Crane with Perturbations

Wu¹,

Chen²,

Yang³

2018

Adaptive Robust Control Systems

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In the presence of persistent perturbations in both unactuated and actuated dynamics of crane systems, an observer-based robust control method is proposed, which achieves the objective of trolley positioning and cargo swing suppression. By dealing with the unactuated and unknown perturbation as an augmented state variable, the system dynamics are transformed into a quasi-chain-of-integrators form based on which a reduced-order augmented-state observer is established to recover the perturbations appearing in the unactuated dynamics. A novel sliding manifold is constructed to improve the robust performance of the control system, and a linear control law is presented to make the state variables stay on the manifold in the presence of perturbations in unactuated dynamics. A Lyapunov function candidate is constructed, and the entire closed-loop system is proved rigorously to be exponentially stable at the equilibrium point. The effectiveness and robustness of the proposed observer-based robust controller are verified by numerical simulation results.

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A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

Chen

Gao

et al. 2019

Asian Journal of Control

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Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti-sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti-sway controlling. KEYWORDS adaptive control, anti-sway control, double-pendulum crane systems, Lyapunov stability theory, neural network control 1 | INTRODUCTION Crane systems with cable hoisting mechanism are typically under-actuated systems. They play an important role in logistics and are widely used in container harbors, railway container yards, and other industrial factories. As cable hoisting mechanism is adopted, the weight of the crane is reduced, but the payload swings unavoidably during the transportation as well as reaching the desired position. The sway of the payload decreases the efficiency of crane's transportation and may result in hazards, such as collision, tip over, and etc. To improve the effectiveness and the safety of crane, researchers proposed many anti-sway control strategies to position the trolley accurately and eliminate the payload sway. All the anti-sway control strategies aim to: i) move the trolley safely, fast, and accurately to the desired position, and ii) decrease the sway of the payload as much as possible during the transportation and eliminate the residual sway when reaching the desired position. TIf the parameters of crane systems are exactly known, open-loop control strategies can work effectively. Singhose et al. reviewed the command shaping/input shaping [1] and implemented the method on doublependulum crane systems [2,3]. It shows that the method

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Parallel neural network combined with sliding mode control in overhead crane control system

Cited by 61 publications

References 11 publications

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

Robust Control of Crane with Perturbations

A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

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