Comparing the Performance of Sway Control Using ZV Input Shaper and LQR on Gantry Cranes

Alhassan, Ahmad Bala; Danapalasingam, Kumeresan A.; Shehu, Muhammad; Abdullahi, Auwalu M.; Shehu, Auwal

doi:10.1109/ams.2015.18

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…where , , , , and have been defined earlier in Table 1, while refers to the angular velocity of the motor. From (8), one easily isolates as…”

Section: Mathematical Modellingmentioning

confidence: 99%

“…Broadly, the existing control strategies in literature can be categorized into three: open-loop (OL), closed-loop (CL), and hybrid control (HC) schemes. For the OL schemes, the input command shaping (ICS) [2][3][4][5][6][7][8], the filter control [9], the command smoothing techniques (CS) [10], and the trajectory planning (TP) techniques [11][12][13] had been proposed. ICS achieves sway minimization by exciting two or more transient oscillations at different instances that result in effective cancellation of the payload oscillations.…”

Section: Introductionmentioning

confidence: 99%

“…For the PID techniques, the authors used a PID controller for position control and a PD controller for sway suppression. Control techniques using optimal linear quadratic regulator (LQR) were proposed by [8] and constructive results were obtained using linearized crane model. Sun et al [18] developed an adaptive anti-swing control strategy for uncertain double-pendulum cranes.…”

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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“…where , , , , and have been defined earlier in Table 1, while refers to the angular velocity of the motor. From (8), one easily isolates as…”

Section: Mathematical Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Generally, there are two approaches to prevent the sway/oscillation: feedback control and feed-forward control. While feedback control shows robust performance compared to feed-forward approach (Alhassan, et al 2015), it requires additional sensors installed on the cranes, although there are some studies that virtually realize feedback control without sensors, by using simulation (Wada, et al 2017). On the other hand, feed-forward control can be easily implemented without modifications to the cranes, and thus preferred in many applications.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a simple anti-sway control without start-up delay and over-travel

Yamamoto

Watanabe

2019

JAMDSM

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This paper evaluates a simple anti-sway control algorithm for fixed-speed cranes. Anti-sway control of suspended loads under cranes has been widely studied, and input shapers have been found as powerful tools. Whereas typical input shapers require variable-speed control for cranes, unity-magnitude (UM) shaper can be applied to fixed-speed cranes. However, UM shaper introduces a start-up delay and over-travel, and thus might not be user-friendly if applied to human-operated cranes. In this work, much simpler anti-sway algorithm for fixed-speed cranes with non-damped oscillations is implemented and evaluated in terms of its robustness. The algorithm is based on the conventional zero-vibration (ZV) shaper, and consists of two sub methods. In the first sub method, the command from an operator is subdivided into a pulse train, which is then fed to ZV shaper. Resulting shaped command is a sum of the original command and one short pulse to suppress the residual oscillation, which is called suppression pulse. Although the command cannot suppress sway during transportation, the crane operated based on the command does not cause start-up delay. On the other hand, the command creates over-travel due to the suppression pulse. To eliminate the over-travel, the second sub method adds a rewind pulse, which is also fed to ZV shaper before being added. The resulting total command exhibits no start-up delay and no over-travel. Experiments using a miniature experimental crane verified the suppression of the residual oscillation, as well as no start-up delay and no over-travel. The paper analyzes its sensitivity against the modeling error. The analyses show that the method is more sensitive to the modeling error when the operation command has a longer duration. Therefore, the algorithm would be effective for cranes with a short-traveling time and/or a long oscillation period.

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“…Many control loop strategies were used to improve a GCS performance. Some examples are the state feedback controller (SFB) using the Ackerman formula, the PID controller [18], sliding mode control (SMC), adaptive controllers [19], Fuzzy Control System [20], and Linear Quadratic Regulator (LQR) [21]. However, the aforementioned controllers are not fully suitable, since most of them do not consider system constraints, and they do not have their parameters obtained from an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Explicit GPC Control Applied to an Approximated Linearized Crane System

Fonseca

Dantas

Dórea

et al. 2019

Journal of Control Science and Engineering

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This paper proposes a MIMO Explicit Generalized Predictive Control (EGPC) for minimizing payload oscillation of a Gantry Crane System subject to input and output constraints. In order to control the crane system efficiently, the traditional GPC formulation, based on online Quadratic Programming (QP), is rewritten as a multiparametric quadratic programming problem (mp-QP). An explicit Piecewise Affine (PWA) control law is obtained and holds the same performance as online QP. To test effectiveness, the proposed method is compared with two GPC formulations: one that handle constraints (CGPC) and another that does not handle constraints (UGPC). Results show that both EGPC and CGPC have better performance, reducing the payload swing when compared to UGPC. Also both EGPC and CGPC are able to control the system without constraint violation. When comparing EGPC to CGPC, the first is able to calculate (during time step) the control action faster than the second. The simulations prove that the overall performance of EGPC is superior to the other used formulations.

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Comparing the Performance of Sway Control Using ZV Input Shaper and LQR on Gantry Cranes

Cited by 7 publications

References 17 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

Evaluation of a simple anti-sway control without start-up delay and over-travel

Explicit GPC Control Applied to an Approximated Linearized Crane System

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