Dynamic control of a six degree-of-freedom robot manipulator using neural networks

Author(s):  
S.-Y. Oh ◽  
M.-J. Joe
2000 ◽  
Author(s):  
Chunhao Joseph Lee ◽  
Constantinos Mavroidis

Abstract This paper presents robust and optimal control methods to suppress vibrations of flexible payloads carried by robotic systems. A new improved estimator in discrete-time H2 optimal control design based on the Kalman Filter predictor form is developed here. Two control design methods using state-space models, LQR and H2 Optimal Design, in discrete-time domain are applied and compared. The manipulator joint encoders and the wrist-mounted six-degree-of-freedom force/torque sensor provide the control feedback. A complete dynamic model of the robot/payload system is taken into account to synthesize the controllers. Experimental verifications of both methods are performed using a Mitsubishi five-degree-of-freedom robot manipulator that carries a flexible beam. It is shown that both methods damp out the vibrations of the payload very effectively.


Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 781-784 ◽  
Author(s):  
Joseph Constantin ◽  
Chaïban Nasr ◽  
Denis Hamad

The paper introduces artificial neural networks for the conventional control of robotic systems for better tracking performance. Different advanced dynamic control techniques are explained and a new second order recursive algorithm has been developed to tune the weights of the neural network. The problem of real-time control of a Pendubot system in difficult situations has been addressed. Examples, such as positioning and balancing structures, are presented and performances are compared to a conventional PD controller.


Author(s):  
Dan Zhang ◽  
Zhen Gao

Optimizing the performances of parallel manipulators by adjusting the structure parameters can be a difficult and time-consuming exercise especially when the parameters are multifarious and the objective functions are too complex. Artificial intelligence approaches can be investigated as the effective criteria to address this issue. In this paper, genetic algorithms and artificial neural network are implemented as the intelligent optimization criteria of global stiffness and dexterity for spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of global stiffness and dexterity are calculated and deduced according to the kinetostatic model. Neural networks are utilized to model the solutions of performance indices. Multi-objective optimization is developed by Pareto-optimal solution. The effectiveness of the proposed methodology is proved by simulation.


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