Stable Adaptive Compensation with Fuzzy Cerebellar Model Articulation Controller for Overhead Cranes

This chapter proposes a novel control strategy for overhead cranes. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy cerebellar model articulation controller (CMAC) is used to compensate friction, and gravity, as well as the coupling between position and anti-swing control. Using a Lyapunov method and an input-to-state stability technique, the controller is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers.

[1]  Kamal A. F. Moustafa,et al.  Nonlinear Modeling and Control of Overhead Crane Load Sway , 1988 .

[2]  Lin Chun-Shin,et al.  CMAC with General Basis Functions. , 1996, Neural networks : the official journal of the International Neural Network Society.

[3]  Eduardo Sontag,et al.  On characterizations of the input-to-state stability property , 1995 .

[4]  Wojciech Blajer,et al.  Motion planning and control of gantry cranes in cluttered work environment , 2007 .

[5]  Ho-Hoon Lee,et al.  Modeling and Control of a Three-Dimensional Overhead Crane , 1998 .

[6]  H. Troger,et al.  Time optimal control of overhead cranes with hoisting of the load , 1987, Autom..

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

[8]  R. Kelly.  Global positioning of robot manipulators via PD control plus a class of nonlinear integral actions , 1998, IEEE Trans. Autom. Control..

[9]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[10]  Yoshiyuki Sakawa,et al.  Optimal control of container cranes , 1981, Autom..

[11]  J.-Y. Chen,et al.  Adaptive design of a fuzzy cerebellar model arithmetic controller neural network , 2005 .

[12]  James S. Albus,et al.  I A New Approach to Manipulator Control: The I Cerebellar Model Articulation Controller , 1975 .

[13]  Ho-Hoon Lee,et al.  A fuzzy-logic antiswing controller for three-dimensional overhead cranes. , 2002 .

[14]  Xiaoou Li,et al.  PD Control of Overhead Crane Systems with Neural Compensation , 2006, ISNN.

[15]  Frank L. Lewis,et al.  Nonlinear feedback control of a gantry crane , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[16]  Hiok Chai Quek,et al.  FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC , 2006, IEEE Transactions on Neural Networks.

[17]  Suguru Arimoto,et al.  A New Feedback Method for Dynamic Control of Manipulators , 1981 .

[18]  Warren P. Seering,et al.  Residual Vibration Reduction Using Vector Diagrams to Generate Shaped Inputs , 1994 .

[19]  Warren E. Dixon,et al.  Nonlinear coupling control laws for an underactuated overhead crane system , 2003 .

[20]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[21]  Ho-Hoon Lee,et al.  A New Motion-Planning Scheme for Overhead Cranes With High-Speed Hoisting , 2004 .

[22]  Zne-Jung Lee,et al.  Robust and fast learning for fuzzy cerebellar model articulation controllers , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Alessandro Giua,et al.  An implicit gain-scheduling controller for cranes , 1998, IEEE Trans. Control. Syst. Technol..

[24]  J. Levine,et al.  A simple output feedback PD controller for nonlinear cranes , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[25]  Chun-Shin Lin,et al.  CMAC with General Basis Functions , 1996, Neural Networks.

[26]  Juan A. Méndez,et al.  An Application of a Neural Self-Tuning Controller to an Overhead Crane , 1999, Neural Computing & Applications.

[27]  Oliver Sawodny,et al.  An automated gantry crane as a large workspace robot , 2002 .

[28]  Rafael Kelly,et al.  A tuning procedure for stable PID control of robot manipulators , 1995, Robotica.

[29]  Meng Joo Er,et al.  Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[30]  Mark W. Noakes,et al.  Generalized inputs for damped-vibration control of suspended payloads , 1992, Robotics Auton. Syst..

[31]  Daming Shi,et al.  FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Ali H. Nayfeh,et al.  Dynamics and Control of Cranes: A Review , 2003 .

[33]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[34]  A. Isidori,et al.  Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems , 1991 .

[35]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[36]  Rebecca Y. M. Wong,et al.  Improving Quality of Crane-Lorry Assignments With Constraint Programming , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).