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Research Papers

A Continuous Robust Antiswing Tracking Control Scheme for Underactuated Crane Systems With Experimental Verification

[+] Author and Article Information
Ning Sun

Assistant Professor
Mem. ASME
Institute of Robotics and Automatic
Information System,
Tianjin Key Laboratory of Intelligent Robotics,
Nankai University,
Tianjin 300353, China
e-mail: sunn@nankai.edu.cn

Yongchun Fang

Professor
Mem. ASME
Institute of Robotics and Automatic Information System,
Tianjin Key Laboratory of Intelligent Robotics,
Nankai University,
Tianjin 300353, China
e-mail: fangyc@nankai.edu.cn

He Chen

Institute of Robotics and
Automatic Information System,
Tianjin Key Laboratory of Intelligent Robotics,
Nankai University,
Tianjin 300353, China
e-mail: chenh@mail.nankai.edu.cn

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 29, 2015; final manuscript received December 28, 2015; published online February 5, 2016. Assoc. Editor: Zongxuan Sun.

J. Dyn. Sys., Meas., Control 138(4), 041002 (Feb 05, 2016) (12 pages) Paper No: DS-15-1047; doi: 10.1115/1.4032460 History: Received January 29, 2015; Revised December 28, 2015

Disturbances and uncertainties are unfavorable elements that always accompany industrial mechatronic systems including cranes. If not fully considered or properly dealt with, they would badly influence the control system performance and degrade the working efficiency. Though traditional sliding mode control (SMC) methods are powerful to address these issues, they are discontinuous and might bring potential damages to the actuating devices. In addition, most existing methods cannot involve such practical constraints as permitted swing amplitudes, maximum velocity, etc. To resolve these problems, we suggest a novel composite antiswing crane control scheme, which involves time-suboptimal analytical trajectory planning and continuous robust tracking control. More precisely, a new analytical suboptimal trajectory planning algorithm is presented, which can generate analytical swing-free smooth trajectories guaranteeing practical constraints. Then, we design a new nonlinear control law to make the crane follow the planned trajectories with continuous control efforts, ensuring stable asymptotic tracking in the presence of perturbations/uncertainties. As far as we know, this is the first crane control scheme that simultaneously achieves state-constrained time-suboptimal trajectory planning and robust control with continuous control efforts. We implement experiments to examine its practical control performance and robustness as well.

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Figures

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Fig. 1

Schematic illustration for a crane system

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Fig. 2

Schematic diagram for the proposed control scheme

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Fig. 6

The used crane experiment testbed

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Fig. 3

Simulation results for case 1: x(t), θ(t), and Fa(t)

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Fig. 4

Simulation results for case 2: x(t), θ(t), and Fa(t)

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Fig. 5

Simulation results for case 3: x(t), θ(t), and Fa(t)

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Fig. 7

The proposed method in experiment group 1: x(t), θ(t), and Fa(t)

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Fig. 10

The improved SMC method of Almutairi and Zribi [55] in experiment group 1: x(t), θ(t), and Fa(t)

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Fig. 11

The energy-based method [29] in experiment group 1: x(t), θ(t), and Fa(t)

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Fig. 12

Experiment group 2: scenarios 1–3

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Fig. 13

Experiment group 2: scenario 4

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Fig. 8

The proposed method in experiment group 1: χ1(t) and χ˙1(t)

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Fig. 9

The EI robust input shaping method [10] in experiment group 1: x(t), θ(t), and Fa(t)

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