{"references": ["Y. Yamamoto, X. Yun, Effect of the Dynamic Interaction on\nCoordinated Control of Mobile Manipulators, IEEE Transaction on\nRobotics and Automation, pp. 816-824, Vol. 12, No. 5, October 1996.", "Q. Huang, S. Sugano, Manipulator Motion Planning for Stabilizing a\nMobile Manipulator, International Conference on Intelligent Robots and\nsystems, IEEE, pp. 3467-3472, 1995.", "Q. Huang, S. Sugano, and K. Tanie, Motion Planning for a Mobile\nManipulator Considering Stability and Task Constraints, IEEE. pp.\n2192-2198, 1998.", "S. Dubowsky, E.E. Vance, Planning Mobile Manipulator Motions\nConsidering Vehicle Dynamic Stability Constraints, Proc. of the 1989\nIEEE, Int. Con. on Robotics and Automation, pp. 1271-1276, Scottsdale,\nAZ, May 14-19, 1989.", "A. Mohri, S. Furuno, and M. Yamamoto, Trajectory Planning of Mobile\nManipulator with End-Effector's Specified Path, Proceeding of the\nIEEE/RSJ, International Conference on Intelligent Robots and Systems,\npp. 2264-2269, Maui, Hawaii, USA, Oct. 29-Nov. 03, 2001.", "T. Das, I.N. Kar, Design and Implementation of an Adaptive Fuzzy\nLogic-Based Controller for Wheels Mobile Robots, IEEE Transactions\non Control Systems Technology, Vol. 14, No. 3., pp. 501-510, May\n2006.", "F.N. Martins, W.C. Celeste, R. Carelli, M. Sarcinelli-Filho, and T.F.\nBastos-Filho, An Adaptive Controller for Autonomous Mobile Robot\nTrajectory Tracking, Journal of Control Engineering Practice 16, pp.\n1354-1363, 2008.", "Y. Zhao, S.L. BeMent, Kinematics, Dynamics and Control of Wheeled\nMobile Robots, Proceeding of the 1992 IEEE, International Conference\non Robotics and Automation, pp. 91-96, Nice, France- May 1992.", "A. Segovia, M. Garduno, and A. Diaz, Kinematic Design and Control of\na Mobile Robot, Journal of the Mexican Society of Instrumentation,\nInstrumentation and Development, pp. 3-11, Vol. 4 Nr.2/1999.\n[10] J. Godjevac, N. Steele, Neuro-Fuzzy Control of a Mobile Robot, Journal\nof Neurocomputing, 28, pp. 127-143, 1999.\n[11] M.R. Emami, A.A. Goldenberg, and B. Turksen, Fuzzy-Logic Control of\nDynamic Systems: From Modeling to Design, Journal of Engineering\nApplications of Artificial Intelligence, 13, pp. 47-69, 2000.\n[12] K.C. Chiou, S.J. Huang, An Adaptive Fuzzy Controller for Robot\nManipulators, Journal of Mechatronics, 15, pp. 151-177, 2005.\n[13] S. Lin, A.A. Goldenberg, Neural-Network Control of Mobile\nManipulators, IEEE Transactions on Neural Networks, pp. 1121-1133,\n2001.\n[14] Y. Li, Y Liu, Online Fuzzy Logic Control for Tipover Avoidance of\nAutonomous redundant mobile manipulators, International Journal of\nVehicle Autonomous Systems, Vol. 4, No. 1, pp. 24-43, 2006.\n[15] J.B. Mbede, P. Ele, C.M. Mveh-Abia, Y. Toure, V. Graefe, and S. Ma,\nIntelligent Mobile Manipulator Navigation Using Adaptive Neuro-Fuzzy\nSystems, International Journal of Information Sciences-Informatics and\nComputer Science, Vol. 171, Issue, 4, pp. 447-474, May 2005.\n[16] J. Denavit, R.C. Hertenberg, A Kinematic Notation for Lower-Pair\nMechanism Based on Matrices, Journal of App. Mech., pp. 215-221,\nJune 1955.\n[17] A. Meghdari, D. Naderi, and M.R. Alam, Tipover Stability Estimation\nfor Autonomous Mobile Manipulator Using Neural Network, 2004\nJapan-USA Symposium on Flexible Automation, JUSFA 2004,\nColorado, July 19-21, 2004.\n[18] R.L. Haupt, S.E. Haupt, Practical Genetic Algorithms, John Wiley &\nSons, Inc. 1998.\n[19] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and\nMachine Learning. Addison-Wesley Publishing Co. Inc., 1989.\n[20] M. Chen, A.M.S. Zalzala, A Genetic Approach to Motion Planning of\nRedundant Mobile Manipulator Systems Considering Safety and\nConfiguration, Journal of Robotic Systems, Vol. 14, Issue. 7, pp. 529-\n544, 1998.\n[21] A. Ghaffari, H.M. Mirkhani, and M. Najafi, Stability Investigation of a\nClass of Fuzzy Logic Control Systems, IEEE International Symposium\non Intelligent Control (ISIC), Mexico City, September 5-7, 2001."]}

In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the tipover stability of mobile manipulators when they are subjected to predefined trajectories for the end-effector and the vehicle. The controller creates proper configurations for the manipulator to prevent the robot from being overturned. The optimal configuration and thus the most favorable control are obtained through soft computing approaches including a combination of genetic algorithm, neural networks, and fuzzy logic. The proposed algorithm, in this paper, is that a look-up table is designed by employing the obtained values from the genetic algorithm in order to minimize the performance index and by using this data base, rule bases are designed for the ANFIS controller and will be exerted on the actuators to enhance the tipover stability of the mobile manipulator. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm.