Efficient Visual Feedback Method to Control a Three-Dimensional Overhead Crane

This paper presents an efficient method to capture the dynamic movement of a three-dimensional (3-D) overhead crane, enabling it to be controlled by visual feedback in real time with two cheap handy cameras. Two tracking areas and one positioning block in each frame are used to search image features and determine the useful vision information. The depth information of 3-D crane system in images can be also obtained by a tag on the dynamic plant. The presented visual tracking method involves comparison of the lightest or darkest points in the tracking or positioning area of a dynamic object and then computes the necessary trolley position and load swing in 3-D space. Upon tracking, the sensing data are sent to an adaptive fuzzy sliding-mode controller (AFSMC) to derive control power for the crane system. Accordingly, the merits of this AFSMC approach, including robustness of the sliding-mode control and the model-free property of the fuzzy logic for the 3-D crane system, are confirmed. Experimental results verify the improvement of the proposed methodology.

[1]  Jianqiang Yi,et al.  Adaptive sliding mode fuzzy control for a two-dimensional overhead crane , 2005 .

[2]  Ruey-Jing Lian,et al.  Enhanced Adaptive Self-Organizing Fuzzy Sliding-Mode Controller for Active Suspension Systems , 2013, IEEE Transactions on Industrial Electronics.

[3]  Cheng-Yuan Chang,et al.  Real-Time Visual Tracking and Measurement to Control Fast Dynamics of Overhead Cranes , 2012, IEEE Transactions on Industrial Electronics.

[4]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[5]  Jangmyung Lee,et al.  Decoupled Dynamic Control for Pitch and Roll Axes of the Unicycle Robot , 2013, IEEE Transactions on Industrial Electronics.

[6]  Jianwei Zhang,et al.  Intelligent Lighting Control for Vision-Based Robotic Manipulation , 2012, IEEE Transactions on Industrial Electronics.

[7]  Honghai Liu,et al.  Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach , 2013, IEEE Transactions on Industrial Electronics.

[8]  Tong Heng Lee,et al.  Design and Implementation of a Takagi–Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot , 2013, IEEE Transactions on Industrial Electronics.

[9]  S. Y. Chen,et al.  Kalman Filter for Robot Vision: A Survey , 2012, IEEE Transactions on Industrial Electronics.

[10]  Ning Sun,et al.  Dynamics Analysis and Nonlinear Control of an Offshore Boom Crane , 2014, IEEE Transactions on Industrial Electronics.

[11]  Ho-Hoon Lee Motion planning for three-dimensional overhead cranes with high-speed load hoisting , 2005 .

[12]  Ning Sun,et al.  New Energy Analytical Results for the Regulation of Underactuated Overhead Cranes: An End-Effector Motion-Based Approach , 2012, IEEE Transactions on Industrial Electronics.

[13]  Cheng-Yuan Chang,et al.  Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance , 2007, IEEE Transactions on Industrial Informatics.