An energy-optimal solution for control of double pendulum cranes

Significance 

Rapid advancement in technology has led to the creation of mechatronic systems that have been widely used in modern industries. It is also the basis for the industrial automation witnessed today. Underactuated mechatronic systems, in particular, have been commonly used in crane transportation for decades. They have several advantages including simple mechanical structures, low energy consumption, and low products costs among others. To improve the functionalities of these systems, several studies both theoretically and practically have been conducted. For example, various control strategies such as using nonlinear controllers and sliding mode-based controllers have been presented for overhead cranes.

In most cases, the overhead crane systems exhibit double pendulum characteristics due to the large mass and the hook’s center of gravity that does not coincide with the payload. The systems also have the number of independent inputs less than the degree of freedom. This renders most of the traditional single pendulum controllers unsuitable for crane transportation. To the end, controllers for double pendulum cranes have been widely investigated with the aim of optimizing their performances, operation safety, and costs. For instance, adaptive tracking controllers have been designed for cranes subjected to external disturbances while the input shaping technology has been used to suppress the dynamics resulting from the oscillations.

Also, various intelligent controllers have been incorporated in use. Although the existing controllers for double pendulum cranes have shown significant improvements, they have not taken much consideration in the energy consumptions of such systems which is a crucial design concern today.

A group of researchers at Nankai University: Associate Professor Ning Sun, Yiming Wu, He Chen and Professor Yongchun Fang proposed an energy-optimal solution for double-pendulum crane systems controllers. The aim was to minimize the energy consumption in moving the trolley from one location to the desired location while at the same time eliminating the double pendulum swings and enhancing safety by keeping the state variable within the given boundaries. This work is a significant breakthrough in the field of crane automation, and it is published in the journal, Mechanical Systems and Signal Processing.

Briefly, for the experiment purpose, the authors first used the Lagrange’s method to obtain the systems dynamics, from which the energy consumption function was constructed. The control problem was then formulated as a quadratic programming problem after the necessary transformations and calculations of the energy consumption equation. Eventually, the quadratic programming problem was solved using a convex optimization technique after which it was implemented on a double pendulum crane for performance verification of the controller.

The authors successfully used the proposed energy-optimal solution to achieve the transportation objectives such as the swing elimination with minimal energy consumption in double pendulum cranes. The system also satisfied the set state constraints while effectively suppressing the residual oscillations. Furthermore, simulation and hardware experimental results provided signified the reliability of the systems controller. Currently, the authors are applying the proposed method to a 32-ton industrial crane to improve its performance.

The study is the first to be reported about optimal-energy consumption in double pendulum cranes with various state and control constraints. Due to its reliability and efficient performance, it will advance transportation by under-actuated double pendulum cranes in the various fields because of the superior’s controllers as compared to other comparative controllers.

An energy-optimal solution for control of double pendulum cranes. Advances in Engineering

About the author

Ning Sun received the B.S. degree in measurement & control technology and instruments (with honors) from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering (with honors) from Nankai University, Tianjin, China, in 2014. He is currently an Associate Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. His research interests include cranes, wheeled robots, magnetic suspension systems, and nonlinear control with applications to mechatronic systems.

Dr. Sun is an organizing/program committee member for several international conferences. He received the First Class Prize of Natural Science Award from the Chinese Association for Artificial Intelligence (CAAI) in 2017, the Golden Patent Award of Tianjin in 2017, the Outstanding Ph.D. Dissertation Award from the Chinese Association of Automation (CAA) in 2016, the Best Application Paper Award from the 31st Youth Academic Annual Conference of CAA in 2016, and the Nomination Award of the Guan Zhao-Zhi Best Paper Award at the 32nd Chinese Control Conference in 2013.

About the author

Yiming Wu received the B.S. degree in intelligent science and technology from Nankai University, Tianjin, China, in 2016. She is currently working towards the Ph.D. degree in control science and engineering, under the supervision of Dr. Ning Sun, with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China.
Her research interests include the control of overhead cranes, RTAC systems, and cherrypickers.

About the author

He Chen received the B.S. degree in automation from Nankai University, Tianjin, China, in 2013. He is currently working towards the Ph.D. degree in control science and engineering with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China.
His research interests include control of mechatronics, overhead cranes, and wheeled mobile robots.

About the author

Yongchun Fang received the B.S. degree and the M.S. degree in control theory and applications from Zhejiang University, Hangzhou, China, in 1996 and 1999, respectively, and the Ph.D. degree in electrical engineering from Clemson University, Clemson, SC, in 2002.

From 2002 to 2003, he was a Postdoctoral Fellow with the Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY. He is currently a Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. His research interests include nonlinear control, visual servoing, control of underactuated systems, and AFM-based nano-systems.

He is a Distinguished Professor of Changjiang Scholars Program and received the National Science Fund for Distinguished Young Scholars of China in 2013.

Reference

Sun, N., Wu, Y., Chen, H., & Fang, Y. (2018). An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments. Mechanical Systems and Signal Processing, 102, 87-101.

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