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Robotic Tower Crane Modeling and Control (RTCMC) with LQR-DRO and LQR-LEIC for Linear and Nonlinear Payload Swing Minimization

Thein Moe Win(1*), Timothy Hesketh(2), Raymond Eaton(3)

(1) School of Electrical Engineering & Telecommunication, University of New South Wales, Australia
(2) School of Electrical Engineering & Telecommunication, University of New South Wales, Australia
(3) School of Electrical Engineering & Telecommunication, University of New South Wales, Australia
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v9i2.8431

Abstract


Fast and accurate positioning and swing minimization of payloads in large standing tall tower crane operation are challenging as well as conflicting tasks. Juggling the trolley back-and-forth manually by crane operator to suppress payload swing can make time consuming and cause fatigue and subsequently cause the crane collapse as well risk the whole working environment. Motivated by Robotic Tower Crane Modeling Control (RTCMC), this work investigates solutions where swing suppression is critical for highly nonlinear trolley-tower-payload crane operation and therefore this work proposes a range of issues in implementing RTCMC. Firstly, recent work of SimMechanics-visualized RTC model and its optimized mathematical linear model are briefly introduced for further controller designs. Secondly, to actively reject the disturbances caused by undesired source of inputs or unknown dynamics, LQR-Disturbance Rejection Observer (DRO) Control with Luenberger-based Extended State Observer is introduced. This research further examines the combination of error space approach with estimator, from which it is argued that the LQR-Estimator-Integral Control (LEIC) and LEIC-Antiwindup for linear model are necessary to achieve robust tracking. Finally, in order to achieve robust tracking control of highly nonlinear trolley translation-payload swing working environment fueled by wind disturbance, LQR-DRO control with torque compensator actuation is implemented on the interaction joints between trolley and payload cables. Proposed RTCMC demonstrated the ability to iteratively achieve desired trolley translation-loadswing geometry. Under this iterative method, all weighting Q-R matrices, Observer gains (L) matrix, and uncertainties gains have adapted to deferent input conditions until pre-specified trajectories of trolley-loadswing are achieved. Evidence of improvements in linear model controls using (LQR-DRO, LQR-LEIC, and LEIC-Antiwindup), and in nonlinear RTCMC using (LQR-DRO) are presented. Control solutions in this research focused on simplicity of implementation: general and straightforward reference-tracking control methods are preferred over Tower crane-tailored formulation. The benefit is that, the proposed RTCMC has potential applications to other types of crane operations and global crane research.
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Keywords


Robotic Tower Crane Modeling Control; Disturbance Rejection Observer; LQR-Estimator-Integral Control; Linear; Nonlinear

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References


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