Fuzzy neural network control for robot manipulator directly driven by switched reluctance motor

Applications of switched reluctance motor (SRM) to direct drive robot are increasingly popular because of its valuable advantages. However, a greatest potential defect its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network (FNN) is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6 pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.

[1]  Athanasios V. Vasilakos,et al.  Ambient Intelligence on the Dance Floor , 2009, Int. J. Cogn. Informatics Nat. Intell..

[2]  S. K. Panda,et al.  Low Torque Ripple Control of Switched Reluctance Motors Using Iterative Learning , 2001, IEEE Power Engineering Review.

[3]  Mark A. Smith,et al.  Functions of Unconscious and Conscious Emotion in the Regulation of Implicit and Explicit Motivated Behavior , 2011 .

[4]  Chien Chern Cheah,et al.  Neural Network Control of Multifingered Robot Hands Using Visual Feedback , 2009, IEEE Transactions on Neural Networks.

[5]  J Cai,et al.  A Novel BVC-RBF Neural Network Based System Simulation Model for Switched Reluctance Motor , 2011, IEEE Transactions on Magnetics.

[6]  Yingxu Wang.  Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing , 2011 .

[7]  M.M. Negm,et al.  Digital control of direct drive robot manipulators , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[8]  Yifeng Zhu,et al.  Rotor position sensorless control of the switched reluctance motor drive for the direct-drive joint of the robot , 2003, Sixth International Conference on Electrical Machines and Systems, 2003. ICEMS 2003..

[9]  Yixin Zhong.  A Cognitive Approach to the Mechanism of Intelligence , 2008, Int. J. Cogn. Informatics Nat. Intell..

[10]  Aurora Vizcaíno,et al.  An Agent System to Manage Knowledge in CoPs , 2009, Int. J. Cogn. Informatics Nat. Intell..

[11]  Xudong Guan,et al.  Main retina information processing pathways modeling , 2010, IEEE ICCI.

[12]  Ouassima Akhrif,et al.  ADAPTIVE FEEDBACK LINEARIZATION FOR POSITON CONTROL OF A SWITCHED RELUmANCE MOTOR : ANALYSIS AND SIMULATION , 1992 .

[13]  Yingxu Wang.  Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (Advances in Cognitive Informatics and Natural Intelligence (Acini) Book Series) , 2009 .

[14]  Frank L. Greitzer,et al.  Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction , 2007, Int. J. Cogn. Informatics Nat. Intell..

[15]  T. Lachman,et al.  Nonlinear modelling of switched reluctance motors using artificial intelligence techniques , 2004 .

[16]  Peng-Yung Woo,et al.  Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators , 2006, IEEE Transactions on Industrial Electronics.

[17]  Rong-Jong Wai,et al.  Design of Dynamic Petri Recurrent Fuzzy Neural Network and Its Application to Path-Tracking Control of Nonholonomic Mobile Robot , 2009, IEEE Transactions on Industrial Electronics.

[18]  S. Peresada,et al.  Feedback linearizing control of switched reluctance motors , 1987 .

[19]  Tsau Young Lin,et al.  Some Remarks on the Concept of Approximations from the View of Knowledge Engineering , 2010, Int. J. Cogn. Informatics Nat. Intell..

[20]  Zhang Yi,et al.  Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[21]  Yingxu Wang,et al.  On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence , 2009, Int. J. Softw. Sci. Comput. Intell..

[22]  Manuel Núñez,et al.  A Formal Specification of the Memorization Process , 2007, Int. J. Cogn. Informatics Nat. Intell..

[23]  Yingxu Wang.  Novel Approaches in Cognitive Informatics and Natural Intelligence , 2008 .

[24]  Maria Semeniuk-Polkowska,et al.  On Foundations and Applications of the Paradigm of Granular Rough Computing , 2008, Int. J. Cogn. Informatics Nat. Intell..

[25]  Li-Chen Fu,et al.  Adaptive decentralized control of robot manipulators driven by current-fed induction motors , 2005 .

[26]  Luc Schneider.  Scientific Authorship and E-commons , 2010 .

[27]  Rong-Jong Wai,et al.  Hybrid fuzzy neural-network control for nonlinear motor-toggle servomechanism , 2002, IEEE Trans. Control. Syst. Technol..

[28]  Michael R. W. Dawson,et al.  Artificial Neural Networks that Classify Musical Chords , 2008, Int. J. Cogn. Informatics Nat. Intell..

[29]  Roberto Cárdenas,et al.  Sensorless Control for a Switched Reluctance Wind Generator, Based on Current Slopes and Neural Networks , 2009, IEEE Transactions on Industrial Electronics.

[30]  Simon G. Fabri,et al.  Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Yingxu Wang.  Cognitive Informatics for Revealing Human Cognition: Knowledge Manipulations in Natural Intelligence , 2012 .

[32]  Yingxu Wang,et al.  The Cognitive Informatics Theory and Mathematical Models of Visual Information Processing in the Brain , 2009, Int. J. Cogn. Informatics Nat. Intell..

[33]  Ruth Milman,et al.  Adaptive control of variable reluctance motors: a spline function approach , 1998, IEEE Trans. Ind. Electron..

[34]  Dong-Seok Hyun,et al.  Speed control of high-performance brushless DC motor drives by load torque estimation , 2003, IEEE 34th Annual Conference on Power Electronics Specialist, 2003. PESC '03..

[35]  A. Paulo Coimbra,et al.  SVR Versus Neural-Fuzzy Network Controllers for the Sagittal Balance of a Biped Robot , 2009, IEEE Transactions on Neural Networks.

[36]  T. Gevers,et al.  Communication and Automatic Interpretation of Affect from Facial Expressions , 2010 .

[37]  Jordi Vallverd,et al.  Thinking Machines and the Philosophy of Computer Science: Concepts and Principles , 2010 .

[38]  Toshio Tsuji,et al.  A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks , 2009, IEEE Transactions on Robotics.

[39]  Yiyu Yao,et al.  Perspectives on Cognitive Informatics and Cognitive Computing , 2010, Int. J. Cogn. Informatics Nat. Intell..

[40]  David G. Taylor,et al.  Low-torque-ripple switched reluctance motors for direct-drive robotics , 1991, IEEE Trans. Robotics Autom..

[41]  Dong Xu,et al.  Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network , 2009, Int. J. Cogn. Informatics Nat. Intell..

[42]  Yiyu Yao,et al.  Interactive classification using a granule network , 2005, Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005)..

[43]  Zhi Liu,et al.  Fuzzy Logic-based Neural Modeling and Robust Control for Robot , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[44]  Yingxu Wang,et al.  On Cognitive Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[45]  Rong-Jong Wai,et al.  Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking , 2010 .

[46]  Ruth Milman,et al.  Observer-based adaptive control of a variable reluctance motor: Experimental results , 1999, IEEE Trans. Control. Syst. Technol..

[47]  Long Cheng,et al.  Multicriteria Optimization for Coordination of Redundant Robots Using a Dual Neural Network , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[48]  Sarangapani Jagannathan,et al.  Neural Network Output Feedback Control of Robot Formations , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).