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Open AccessArticle

Real-Time Robust and Optimized Control of a 3D Overhead Crane System

School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
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Sensors 2019, 19(15), 3429; https://doi.org/10.3390/s19153429
Received: 8 July 2019 / Revised: 30 July 2019 / Accepted: 30 July 2019 / Published: 5 August 2019
(This article belongs to the Special Issue Sensors and Robot Control)
A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, a new and simplified model is developed where the overhead crane actuators are used to design the controller, with all the nonlinear equations of motions being viewed as disturbances affecting each actuator. A feedforward control is then designed to tackle these disturbances via computed torque control technique. A new load swing control is designed along with a new motion planning scheme to robustly minimize load swings as well as allowing fast load transportation without violating system’s constraints through updating reference trolley accelerations. The stability and performance analysis of the proposed discrete-time control system are demonstrated and validated analytically and practically. View Full-Text
Keywords: robot control; robotic systems modeling; high-gain observers for robotic systems; position sensors; computed torque control; feedforward control; motion planning; 3D overhead crane; passivity and ℒ2 stability; trajectory tacking robot control; robotic systems modeling; high-gain observers for robotic systems; position sensors; computed torque control; feedforward control; motion planning; 3D overhead crane; passivity and ℒ2 stability; trajectory tacking
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MDPI and ACS Style

Khatamianfar, A.; Savkin, A.V. Real-Time Robust and Optimized Control of a 3D Overhead Crane System. Sensors 2019, 19, 3429.

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