[en] This paper addresses the control problem for trajectory tracking of a class of robotic manipulators presenting uncertainties and switching constraints using a biomimetic approach. Uncertainties, system-inherent as well as environmental disturbances deteriorate the performance of the system. A change in constraints between the robot’s end-effector and the environment resulting in a switched nonlinear system, undermines the stable system performance. In this work, a robust adaptive controller combining sliding mode control and BELBIC (Brain Emotional Learning-Based Intelligent Control) is suggested to remediate the expected impacts on the overall system tracking performance and stability. The controller is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences. The proposed control algorithm is designed to be applicable to discontinuous freeform geometries. Its stability is proven theoretically and a simulation, performed on a two-link manipulator verifies its efficacy.
Disciplines :
Ingénierie mécanique
Auteur, co-auteur :
KLECKER, Sophie ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
PLAPPER, Peter ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
BELBIC-Sliding Mode Control of Robotic Manipulators with Uncertainties and Switching Constraints
Date de publication/diffusion :
novembre 2016
Nom de la manifestation :
ASME 2016 International Mechanical Engineering Congress and Exposition IMECE 2016
Organisateur de la manifestation :
ASME
Lieu de la manifestation :
Phoenix, Etats-Unis
Date de la manifestation :
from 11-11-2016 to 17-11-2016
Manifestation à portée :
International
Titre de l'ouvrage principal :
Proceedings of the ASME 2016 International Mechanical Engineering Congress and Exposition
Peer reviewed :
Peer reviewed
Intitulé du projet de recherche :
R-AGR-0071 - IRP13 - PROBE (20130101-20151231) - PLAPPER Peter
Raibert M. H., Craig J. J., 1981, "Hybrid Position/Force Control of Manipulators", J. Dyn. Sys. Meas., Control 103(2), pp.126-133.
Chiu C., Lian K., Wu T., 2004, "Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators", Automatica 40(12), pp.2111-2119.
Ha I., Han M., 2004, "Robust Hybrid Position/Force Control with Adaptive Scheme", JSME International Journal 47(4), pp.1161-1165.
Kiguchi K., Fukuda T., 2000, "Position/force control of robot manipulators for Geometrically Unknown Objects Using Fuzzy Neural Networks", IEEE Transactions on Industrial Electronics 47(3), pp.641-649.
Fanaei A., Farrokhi M., 2006, "Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment", Journal of Intelligent and Fuzzy Systems 17, pp.125-144.
Liberzon D., 2003, "Switching in Systems and Control", Birkhäuser.
Pagilla P. R., Yu B., 2001, "Adaptive control of robotic surface finishing processes", Proceedings of the American Control Conference, pp.630-635.
Yu L., Fei S., Huang J., Yu G., 2014, "Trajectory Switching Control of Robotic Manipulators Based on RBF Neural Networks", Circuits, Systems and Signal Processing 33(4), pp.1119-1133.
Jasim I. F., Plapper P. W., 2013, "Adaptive sliding mode control of switched constrained robotic manipulators", 11th IEEE International Conference on Industrial Informatics (INDIN), pp.305-310.
Van Cuong P., Nan W. Y., 2015, "Adaptive trajectory tracking neural network control with robust compensator for robot manipulators", Neural Computing and Applications, DOI 10.1007/s00521-015-1873-4.
Roopaei M., Zolghadri Jahromi M., 2009, "Chattering-free fuzzy sliding mode control in MIMO uncertain systems", Nonlinear Analysis 71(10), pp.4430-4437.
Li T. S., Huang Y., 2010, "MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators", Information Sciences 180(23), pp.4641-4660.
Li Y., Yang G., 2015, "Robust fuzzy adaptive fault-tolerant control for a class of nonlinear systems with mismatched uncertainties and actuator faults", Nonlinear Dynamics 81, pp.395-409.
Lucas C., Shahmirzadi D., Sheikholeslami N., 2004, "Introducing BELBIC: brain emotional learning based intelligent controller", Intelligent Automation and Soft Computing 10(1), pp.11-22.
Morén J., Balkenius C., 2000, "A computational model of emotional learning in the amygdala", Proceedings of the 6th International Conference on Simulation of Adaptive Behavior 32.
Balkenius C., Morén J., 2001, "Emotional Learning: a Computational Model of the Amygdala", Cybernetics and Systems 32(6), pp.611-636.
Aghaee S. A., Lucas C., Zadeh K. A., 2012, "Applying Brain Emotional Learning Based Intelligent Controller (Belbic) to Multiple-Area Power Systems", Asian Journal of Control 14(6), pp.1580-1588.
Daryabeigi E., Abjadi N. R., Arab Markadeh G. R., 2014, "Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC)", Journal of Intelligent and Fuzzy Systems 26, pp.1879-1892.
Yi H., 2015, "A Sliding Mode Control Using Brain Limbic System Control Strategy for a Robotic Manipulator", International Journal of Advanced Robotic Systems 12(158).
Liu J., Wang X., 2011, Advanced Sliding Mode Control for Mechanical Systems: Design, Analysis and MATLAB Simulation, Tsinghua University Press, Beijing, Springer-Verlag Berlin Heidelberg.
Slotine J. E., Li W., 1991, Applied Nonlinear Control, Prentice Hall, Upper Saddler River, NJ.
Shah H., Chakravarty S., 2009, Kinematic and Dynamic Control of a Two Link Manipulator.
