Active-Passive Combined Control System in Crane Type for Heave Compensation

Shi, Mingjiang; Guo, Shun; Jiang, Lu; Huang, Zhiqiang

doi:10.1109/access.2019.2950703

Cited by 20 publications

(6 citation statements)

References 19 publications

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“…However, the rareness of the ideal conditions has made closed-loop control with high precision and insensitivity to external disturbance and system parameter changes to be more commonly applied in practical engineering. For this, many domestic and foreign scholars have carried out a lot of researches, such as PID control [17], adaptive control [18][19], sliding mode control [20][21][22], fuzzy control [23][24], passive control [25][26] and online trajectory planning [27][28]. To be more specific, the literature [17], in order to reduce the errors of vehicle positioning caused by uncertain external disturbances and unmodeled dynamics, expounded a quasi-PID controller without object parameters, which can effectively guarantee the stability of the system.…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Sliding Mode Control Method for Distributed-Mass Double Pendulum Bridge Crane With Variable Parameters

et al. 2021

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This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle's invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn(S), the designed continuous function th(S) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.INDEX TERMS Bridge crane, distributed-mass, time-varying sliding mode control, LaSalle's invariance principle, asymptotic stability, global robustness.

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Section: Introductionmentioning

confidence: 99%

A Time-Varying Sliding Mode Control Method for Distributed-Mass Double Pendulum Bridge Crane With Variable Parameters

et al. 2021

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“…It has the anti-interference ability and is suitable for the nonlinear system that ordinary PID control does not have. 30 The model diagram is shown in Figure 10. However, the design of fuzzy PID control lacks systematicity, and the adjustment of parameters requires strong experience and poor self-learning ability.…”

Section: Electric Drive Ocean Traction Winch Cable Constant Tension C...mentioning

confidence: 99%

BP fuzzy neural network PID based constant tension control of traction winch

Zhu

Zhang

Zhao

et al. 2022

Measurement and Control

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As part of the ocean winch, the traction winch mainly plays the role of attenuating the load tension. It generally uses simple speed control. For the ship-based collection and release system, complex sea conditions make the cable tension constantly changes. The traditional control method will make the cable in the traction winch too slack or too tight. Meanwhile, it can result in the wrong rope, bite rope and slippage, and other phenomena. Eventually, it is leading to cable damage or breakage and equipment loss. Moreover, with the introduction of expensive photoelectric composite cables, there is a greater need for a good control algorithm to extend the life of the cable and ensure the safety of retrieval and release. Therefore, this paper focuses on the constant tension control of the traction winch cable. In this paper, the NARX neural network-based cable tension compensation value prediction model is established as the feedforward part based on historical ship heave displacement data and cable tension compensation data as training data. On this basis, the simulation model of cable constant tension winch based on common PID control, fuzzy PID control, and BP fuzzy neural network PID control are established. By comparing the tension of the cable with the above controller, the results show that the tension control of the cable based on the NARX neural network predicted tension compensation value plus BP fuzzy neural network PID control has high accuracy, fast dynamic response, smooth system, and no overshoot.

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“…Some closed-loop feedback control methods have been proposed to deal with external disturbance to such a system [10]. The common method is to use proportional-integral-derivative (PID) controllers [11]. Some control algorithms based on state observers are proposed to handle partially unmeasurable states [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

Bao

Kang

et al. 2021

Machines

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A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance. Is it possible to learn the dynamics model of a crane that can achieve the best performance, instead of learning its true dynamics? This work answers the question by presenting a performance-driven model predictive control (P-MPC) algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller uses a proportional–integral–derivative controller to achieve anti-sway rapidly. An outer-loop controller uses MPC to ensure accurate trolley positioning under control constraints. Compared with classical MPC, this work proposes a data-driven method for plant modeling and controller parameter updating. By considering the control target at the learning stage, the method can avoid adjusting the controller to deal with uncertainty. We use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing control performance and then used in conjunction with optimal control schemes to efficiently design a controller for a given task. The model is updated directly based on the performance observed in experiments on the physical system in an iterative manner till a desired performance is achieved. The controller parameters and prediction models of the best closed-loop performance can be found through continuous experiments and iterative optimization. Simulation and experiment results show that we can explicitly find the dynamics model that produces the best performance for an actual system, and the method can quickly suppress swing and realize accurate trolley positioning. The results verified its effectiveness, feasibility, and superior performance on comparing it with state-of-the-art methods.

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Active-Passive Combined Control System in Crane Type for Heave Compensation

Cited by 20 publications

References 19 publications

A Time-Varying Sliding Mode Control Method for Distributed-Mass Double Pendulum Bridge Crane With Variable Parameters

A Time-Varying Sliding Mode Control Method for Distributed-Mass Double Pendulum Bridge Crane With Variable Parameters

BP fuzzy neural network PID based constant tension control of traction winch

A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

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