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Determining maximum load-carrying capacity of robots using adaptive robust neural controller

Published online by Cambridge University Press:  22 March 2010

M. H. Korayem ,
A. Alamdari ,
R. Haghighi  and
A. H. Korayem
M. H. Korayem*
Affiliation:
Robotic Research Laboratory, College of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. Alamdari
Affiliation:
Robotic Research Laboratory, College of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
R. Haghighi
Affiliation:
Robotic Research Laboratory, College of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. H. Korayem
Affiliation:
Robotic Research Laboratory, College of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

In this paper, the combination of neural network (NN), proportional derivative (PD), and robust controller are used for determining the maximum load-carrying capacity (MLCC) of articulated robots, subject to both actuator and end-effector deflection constraints. The proposed technique is then applied to articulated robots, and MLCC is obtained for a given trajectory. In the practical simulations, it's impossible to determine the parameters of robot model exactly, so the trajectory tracking performance of the proportional integral derivative (PID) and computed torque methods significantly decrease. The PD control of robot has major problem, it cannot guarantee zero steady state error. For this reason, the NN controller with PD and robust controller are used. The multilayer neural network is also used to compensate gravity and friction effects. By using Lyapunov Direct Method it is shown that the stability of closed loop system would be guaranteed, if the weights of multilayer had certain learning rules. Standard back propagation algorithm is used as a learning algorithm to update the connection weights of the NN controller. The simulation results of the proposed adaptive robust neural network (ARNN) controller are compred with sliding mode and feedback linearization methods for flexible joint robot, and compared with open loop controller for 3D industrial robot. The obtained results assured the robustness and improvement in MLCC in the presence of uncertainties in dynamic model of the robot arm and external disturbances. In fact, adaptive robust NN controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

Keywords

RobotNeural networkTrajectory trackingMaximum loadCapacity
Type
Article
Information
Robotica , Volume 28 , Issue 7 , December 2010 , pp. 1083 - 1093
Copyright
Copyright © Cambridge University Press 2010

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References

1.Thomas, M., Yuan-Chou, H. and Tesar, D., “Optimal actuator sizing for robotic manipulators based on local dynamic criteria,” ASME J. Mech. Trans. Autom. Des. 107, 163–169 (1985).CrossRefGoogle Scholar
2.Korayem, M. H. and Basu, A., “Dynamic load carrying capacity of robotic manipulators with joint elasticity imposing accuracy constraints,” J. Robot. Auton. Syst. 13, 219–229 (1994).Google Scholar
3.Korayem, M. H., Davarpanah, F. and Ghariblu, H., “Load carrying capacity of flexible joint manipulators with feedback linearization,” Int. J. Adv. Manuf. Technol. 21, 389–397 (2006).CrossRefGoogle Scholar
4.Korayem, M. H., Ghariblu, H. and Basu, A., “Dynamic load-carrying capacity of mobile-base flexible joint manipulators,” J. Adv. Manuf. Technol. 25, 62–70 (2005).CrossRefGoogle Scholar
5.Korayem, M. H. and Pilechian, A., “Maximum allowable load of elastic joint robots: Sliding mode control approach,” Amirkabir J. Sci. Technol. 17 (65), 75–82 (2007).Google Scholar
6.Lewis, F., “Neural network feedback control: Work at UTA's Automation and Robotics Research Institute,” J. Intell. Robot. Syst. 48, 513–523 (2007).CrossRefGoogle Scholar
7.Gupta, P. and Sinha, N. K., “Intelligent control of robotic manipulators: Experimental study using neural networks,” J. Mechatronics 10, 289–305 (2000).CrossRefGoogle Scholar
8.Nakanishi, J. and Schaal, S., “Feedback error learning and nonlinear adaptive control,” Neural Netw. 17, 1453–1465 (2004).CrossRefGoogle ScholarPubMed
9.Yildirim, S., “Adaptive robust neural controller for robots,” J. Robot. Auton. Syst. 46, 175–184 (2004).CrossRefGoogle Scholar
10.Temurtasa, F., Temurtas, H. and Yumusak, N., “Application of neural generalized predictive control to robotic manipulators with a cubic trajectory and random disturbances,” Robot. Auton. Syst. 54, 74–83 (2006)Google Scholar
11.Abdelhameed, M. M., “Adaptive neural network based controller for robots,” J. Mechatronics 9, 147–162 (1999).CrossRefGoogle Scholar
12.Sun, F., Sun, Z., Li, N. and Zhang, L., “Stable adaptive control for robot trajectory tracking using dynamic neural networks,” J. Mach. Intell. Robot. Control 2, 71–78 (1999).Google Scholar
13.Akbas, E. and Esin, E., “A simulational comparison of intelligent control algorithms on a direct drive manipulator,” J. Robot. Auton. Syst. 49, 235–244 (2004).CrossRefGoogle Scholar
14.Yu, W. and Li, X., “PD control of robot with velocity estimation and uncertainties compensation,” Int. J. Robot. Autom. 21 (2006).Google Scholar
15.Lewis, F. and Parisini, T., “Neural network feedback control with guaranteed stability,” Int. J. Control 70, 337–339 (1998).CrossRefGoogle Scholar
16.Yu, W., Poznyak, A. S. and Sanchez, E. N., “Neural adaptive control of two-link manipulator with sliding mode,” IEEE Int. Conf. Robot. Autom. 4, 3122–3127 (1999).Google Scholar
17.Chun, H., Guan-Ming, C. and Lee, T., “Robust intelligent tracking control with PID-type learning algorithm,” Neurocomputing 71, 234–243, (2007)Google Scholar
18.Xiaogang, R., Mingxiao, D., Daoxiong, G. and Junfei, Q., “On-line adaptive control for inverted pendulum balancing based on feedback-error-learning,” Neurocomputing 70, 770–776 (2007).Google Scholar
19.Purwar, S., Kar, I. and Jha, A., “Adaptive output feedback tracking control of robot manipulators using position measurements only,” Expert Syst. Appl. 34, 2789–2798 (2008).CrossRefGoogle Scholar