Tipover Stability Enhancement of Wheeled Mobile Manipulators Using an Adaptive Neuro- Fuzzy Inference Controller System
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.
[1] Y. Yamamoto, X. Yun, Effect of the Dynamic Interaction on
Coordinated Control of Mobile Manipulators, IEEE Transaction on
Robotics and Automation, pp. 816-824, Vol. 12, No. 5, October 1996.
[2] Q. Huang, S. Sugano, Manipulator Motion Planning for Stabilizing a
Mobile Manipulator, International Conference on Intelligent Robots and
systems, IEEE, pp. 3467-3472, 1995.
[3] Q. Huang, S. Sugano, and K. Tanie, Motion Planning for a Mobile
Manipulator Considering Stability and Task Constraints, IEEE. pp.
2192-2198, 1998.
[4] S. Dubowsky, E.E. Vance, Planning Mobile Manipulator Motions
Considering Vehicle Dynamic Stability Constraints, Proc. of the 1989
IEEE, Int. Con. on Robotics and Automation, pp. 1271-1276, Scottsdale,
AZ, May 14-19, 1989.
[5] A. Mohri, S. Furuno, and M. Yamamoto, Trajectory Planning of Mobile
Manipulator with End-Effector's Specified Path, Proceeding of the
IEEE/RSJ, International Conference on Intelligent Robots and Systems,
pp. 2264-2269, Maui, Hawaii, USA, Oct. 29-Nov. 03, 2001.
[6] T. Das, I.N. Kar, Design and Implementation of an Adaptive Fuzzy
Logic-Based Controller for Wheels Mobile Robots, IEEE Transactions
on Control Systems Technology, Vol. 14, No. 3., pp. 501-510, May
2006.
[7] F.N. Martins, W.C. Celeste, R. Carelli, M. Sarcinelli-Filho, and T.F.
Bastos-Filho, An Adaptive Controller for Autonomous Mobile Robot
Trajectory Tracking, Journal of Control Engineering Practice 16, pp.
1354-1363, 2008.
[8] Y. Zhao, S.L. BeMent, Kinematics, Dynamics and Control of Wheeled
Mobile Robots, Proceeding of the 1992 IEEE, International Conference
on Robotics and Automation, pp. 91-96, Nice, France- May 1992.
[9] A. Segovia, M. Garduno, and A. Diaz, Kinematic Design and Control of
a Mobile Robot, Journal of the Mexican Society of Instrumentation,
Instrumentation and Development, pp. 3-11, Vol. 4 Nr.2/1999.
[10] J. Godjevac, N. Steele, Neuro-Fuzzy Control of a Mobile Robot, Journal
of Neurocomputing, 28, pp. 127-143, 1999.
[11] M.R. Emami, A.A. Goldenberg, and B. Turksen, Fuzzy-Logic Control of
Dynamic Systems: From Modeling to Design, Journal of Engineering
Applications of Artificial Intelligence, 13, pp. 47-69, 2000.
[12] K.C. Chiou, S.J. Huang, An Adaptive Fuzzy Controller for Robot
Manipulators, Journal of Mechatronics, 15, pp. 151-177, 2005.
[13] S. Lin, A.A. Goldenberg, Neural-Network Control of Mobile
Manipulators, IEEE Transactions on Neural Networks, pp. 1121-1133,
2001.
[14] Y. Li, Y Liu, Online Fuzzy Logic Control for Tipover Avoidance of
Autonomous redundant mobile manipulators, International Journal of
Vehicle Autonomous Systems, Vol. 4, No. 1, pp. 24-43, 2006.
[15] J.B. Mbede, P. Ele, C.M. Mveh-Abia, Y. Toure, V. Graefe, and S. Ma,
Intelligent Mobile Manipulator Navigation Using Adaptive Neuro-Fuzzy
Systems, International Journal of Information Sciences-Informatics and
Computer Science, Vol. 171, Issue, 4, pp. 447-474, May 2005.
[16] J. Denavit, R.C. Hertenberg, A Kinematic Notation for Lower-Pair
Mechanism Based on Matrices, Journal of App. Mech., pp. 215-221,
June 1955.
[17] A. Meghdari, D. Naderi, and M.R. Alam, Tipover Stability Estimation
for Autonomous Mobile Manipulator Using Neural Network, 2004
Japan-USA Symposium on Flexible Automation, JUSFA 2004,
Colorado, July 19-21, 2004.
[18] R.L. Haupt, S.E. Haupt, Practical Genetic Algorithms, John Wiley &
Sons, Inc. 1998.
[19] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning. Addison-Wesley Publishing Co. Inc., 1989.
[20] M. Chen, A.M.S. Zalzala, A Genetic Approach to Motion Planning of
Redundant Mobile Manipulator Systems Considering Safety and
Configuration, Journal of Robotic Systems, Vol. 14, Issue. 7, pp. 529-
544, 1998.
[21] A. Ghaffari, H.M. Mirkhani, and M. Najafi, Stability Investigation of a
Class of Fuzzy Logic Control Systems, IEEE International Symposium
on Intelligent Control (ISIC), Mexico City, September 5-7, 2001.
[1] Y. Yamamoto, X. Yun, Effect of the Dynamic Interaction on
Coordinated Control of Mobile Manipulators, IEEE Transaction on
Robotics and Automation, pp. 816-824, Vol. 12, No. 5, October 1996.
[2] Q. Huang, S. Sugano, Manipulator Motion Planning for Stabilizing a
Mobile Manipulator, International Conference on Intelligent Robots and
systems, IEEE, pp. 3467-3472, 1995.
[3] Q. Huang, S. Sugano, and K. Tanie, Motion Planning for a Mobile
Manipulator Considering Stability and Task Constraints, IEEE. pp.
2192-2198, 1998.
[4] S. Dubowsky, E.E. Vance, Planning Mobile Manipulator Motions
Considering Vehicle Dynamic Stability Constraints, Proc. of the 1989
IEEE, Int. Con. on Robotics and Automation, pp. 1271-1276, Scottsdale,
AZ, May 14-19, 1989.
[5] A. Mohri, S. Furuno, and M. Yamamoto, Trajectory Planning of Mobile
Manipulator with End-Effector's Specified Path, Proceeding of the
IEEE/RSJ, International Conference on Intelligent Robots and Systems,
pp. 2264-2269, Maui, Hawaii, USA, Oct. 29-Nov. 03, 2001.
[6] T. Das, I.N. Kar, Design and Implementation of an Adaptive Fuzzy
Logic-Based Controller for Wheels Mobile Robots, IEEE Transactions
on Control Systems Technology, Vol. 14, No. 3., pp. 501-510, May
2006.
[7] F.N. Martins, W.C. Celeste, R. Carelli, M. Sarcinelli-Filho, and T.F.
Bastos-Filho, An Adaptive Controller for Autonomous Mobile Robot
Trajectory Tracking, Journal of Control Engineering Practice 16, pp.
1354-1363, 2008.
[8] Y. Zhao, S.L. BeMent, Kinematics, Dynamics and Control of Wheeled
Mobile Robots, Proceeding of the 1992 IEEE, International Conference
on Robotics and Automation, pp. 91-96, Nice, France- May 1992.
[9] A. Segovia, M. Garduno, and A. Diaz, Kinematic Design and Control of
a Mobile Robot, Journal of the Mexican Society of Instrumentation,
Instrumentation and Development, pp. 3-11, Vol. 4 Nr.2/1999.
[10] J. Godjevac, N. Steele, Neuro-Fuzzy Control of a Mobile Robot, Journal
of Neurocomputing, 28, pp. 127-143, 1999.
[11] M.R. Emami, A.A. Goldenberg, and B. Turksen, Fuzzy-Logic Control of
Dynamic Systems: From Modeling to Design, Journal of Engineering
Applications of Artificial Intelligence, 13, pp. 47-69, 2000.
[12] K.C. Chiou, S.J. Huang, An Adaptive Fuzzy Controller for Robot
Manipulators, Journal of Mechatronics, 15, pp. 151-177, 2005.
[13] S. Lin, A.A. Goldenberg, Neural-Network Control of Mobile
Manipulators, IEEE Transactions on Neural Networks, pp. 1121-1133,
2001.
[14] Y. Li, Y Liu, Online Fuzzy Logic Control for Tipover Avoidance of
Autonomous redundant mobile manipulators, International Journal of
Vehicle Autonomous Systems, Vol. 4, No. 1, pp. 24-43, 2006.
[15] J.B. Mbede, P. Ele, C.M. Mveh-Abia, Y. Toure, V. Graefe, and S. Ma,
Intelligent Mobile Manipulator Navigation Using Adaptive Neuro-Fuzzy
Systems, International Journal of Information Sciences-Informatics and
Computer Science, Vol. 171, Issue, 4, pp. 447-474, May 2005.
[16] J. Denavit, R.C. Hertenberg, A Kinematic Notation for Lower-Pair
Mechanism Based on Matrices, Journal of App. Mech., pp. 215-221,
June 1955.
[17] A. Meghdari, D. Naderi, and M.R. Alam, Tipover Stability Estimation
for Autonomous Mobile Manipulator Using Neural Network, 2004
Japan-USA Symposium on Flexible Automation, JUSFA 2004,
Colorado, July 19-21, 2004.
[18] R.L. Haupt, S.E. Haupt, Practical Genetic Algorithms, John Wiley &
Sons, Inc. 1998.
[19] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning. Addison-Wesley Publishing Co. Inc., 1989.
[20] M. Chen, A.M.S. Zalzala, A Genetic Approach to Motion Planning of
Redundant Mobile Manipulator Systems Considering Safety and
Configuration, Journal of Robotic Systems, Vol. 14, Issue. 7, pp. 529-
544, 1998.
[21] A. Ghaffari, H.M. Mirkhani, and M. Najafi, Stability Investigation of a
Class of Fuzzy Logic Control Systems, IEEE International Symposium
on Intelligent Control (ISIC), Mexico City, September 5-7, 2001.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59964", author = "A. Ghaffari and A. Meghdari and D. Naderi and S. Eslami", title = "Tipover Stability Enhancement of Wheeled Mobile Manipulators Using an Adaptive Neuro- Fuzzy Inference Controller System", abstract = "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.", keywords = "Mobile Manipulator, Tipover Stability
Enhancement, Adaptive Neuro-Fuzzy Inference Controller System,
Soft Computing.", volume = "2", number = "9", pages = "1070-7", }
