Soft computing methods applied to the control of a flexible robot manipulator

The paper describes use of soft computing methods (fuzzy logic and neural network techniques) in the development of a hybrid fuzzy neural control (HFNC) scheme for a multi-link flexible manipulator. A manipulator with multiple flexible links is a multivariable system of considerable complexity due to the inter-link coupling effects that are present in both rigid and flexible motions. Modelling and controlling the dynamics of such manipulators is therefore difficult. The proposed HFNC scheme generates control actions combining contributions form both a fuzzy controller and a neural controller. The primary loop of the proposed HFNC contains a fuzzy controller and a neural network controller in the secondary loop to compensate for the coupling effects due to the rigid and flexible motion along with the inter-link coupling. It has been ascertained from the present investigation that the proposed soft-computing-based controller works effectively in the tracking control of such a multi-link flexible manipulator. The results are extendable to other multivariable systems of similar complexity.

[1]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[2]  G. Feng,et al.  A Survey on Analysis and Design of Model-Based Fuzzy Control Systems , 2006, IEEE Transactions on Fuzzy Systems.

[3]  Curtis Collins,et al.  Adaptive neuro-fuzzy control of a flexible manipulator , 2005 .

[4]  Heidar Ali Talebi,et al.  A Fuzzy Lyapunov-Based Control Strategy for a Macro–Micro Manipulator: Experimental Results , 2007, IEEE Transactions on Control Systems Technology.

[5]  Mo M. Jamshidi.  Autonomous control of complex systems: robotic applications , 2001, Appl. Math. Comput..

[6]  Fuchun Sun,et al.  Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks , 2002, J. Intell. Robotic Syst..

[7]  Heinz Unbehauen,et al.  A neurofuzzy approach to the control of a flexible-link manipulator , 2002, IEEE Trans. Robotics Autom..

[8]  Heidar Ali Talebi,et al.  Neural network based control schemes for flexible-link manipulators: simulations and experiments , 1998, Neural Networks.

[9]  Alan S. Morris,et al.  Dynamic modelling, simulation and control of a manipulator with flexible links and joints , 2002, Robotics Auton. Syst..

[10]  Stephen Yurkovich,et al.  Rule-based control for a flexible-link robot , 1994, IEEE Trans. Control. Syst. Technol..

[11]  Rong-Jong Wai,et al.  Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network , 2004, IEEE Trans. Fuzzy Syst..

[12]  David Wang,et al.  A fuzzy control strategy for a flexible single link robot , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[13]  Meng Joo Er,et al.  Robust adaptive control of robot manipulators using generalized fuzzy neural networks , 2003, IEEE Trans. Ind. Electron..

[14]  Toshio Fukuda,et al.  Position/force control of robot manipulators for geometrically unknown objects using fuzzy neural networks , 2000, IEEE Trans. Ind. Electron..

[15]  Ali Fallah,et al.  A Neurofuzzy Controller for a Single Link Flexible Manipulator , 2007, ICANN.

[16]  Sun Fuchun,et al.  Observer based adaptive controller design of flexible manipulators using time-delay neuro-fuzzy networks , 2002, WCICA 2002.

[17]  Mohamed B. Trabia,et al.  Design and tuning of importance-based fuzzy logic controller for a flexible-link manipulator , 2006, J. Intell. Fuzzy Syst..

[18]  F. Lewis,et al.  Fuzzy controller for flexible-link robot arm by reduced-order techniques , 2002 .

[19]  Jamil M. Renno,et al.  Inverse Dynamics Based Tuning of a Fuzzy Logic Controller for a Single-Link Flexible Manipulator , 2007 .

[20]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[21]  Shuzhi Sam Ge,et al.  Improving regulation of a single-link flexible manipulator with strain feedback , 1998, IEEE Trans. Robotics Autom..

[22]  Alessandro De Luca,et al.  Closed-form dynamic model of planar multilink lightweight robots , 1991, IEEE Trans. Syst. Man Cybern..

[23]  Fuchun Sun,et al.  Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators , 2003, Fuzzy Sets Syst..

[24]  Manish Kumar,et al.  Neuro-fuzzy control applied to multiple cooperating robots , 2005, Ind. Robot.

[25]  Rong-Jong Wai,et al.  Intelligent optimal control of single-link flexible robot arm , 2004, IEEE Transactions on Industrial Electronics.

[26]  J. Lin,et al.  Fuzzy logic control for flexible link robot arm by singular perturbation approach , 2002, Appl. Soft Comput..

[27]  Derek A. Linkens,et al.  Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications , 1996 .

[28]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[29]  Frank L. Lewis,et al.  Implementation of a neural network tracking controller for a single flexible link: comparison with PD and PID controllers , 1998, IEEE Trans. Ind. Electron..

[30]  Marzuki Khalid,et al.  Neuro-control and its applications , 1996 .

[31]  K. S. Narendra,et al.  Neural networks for control theory and practice , 1996, Proc. IEEE.

[32]  Alan S. Morris,et al.  Genetic Algorithm Tuned Fuzzy Logic Controller for a Robot Arm with Two-link Flexibility and Two-joint Elasticity , 2007, J. Intell. Robotic Syst..

[33]  Eduardo D. Sontag,et al.  Neural Networks for Control , 1993 .

[34]  Alan S. Morris,et al.  Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulator , 2003 .