Robust Trajectory Tracking Control of Elastic Robot Manipulators

1997 ◽  
Vol 119 (4) ◽  
pp. 727-735 ◽  
Author(s):  
Yoshifumi Morita ◽  
Hiroyuki Ukai ◽  
Hisashi Kando

In this paper, a robust control strategy is proposed for the trajectory tracking control of multi-link elastic robot manipulators. The robustness against both of the structured uncertainty caused by the nonlinear mechanical structure and the unstructured one caused by elasticity of links is taken into account in designing controllers. For this purpose the model of elastic robot manipulators is decomposed into the slow model and the fast model by using an integral manifold approach. The slow controller, which is robust against the structured uncertainty, is designed for the slow model on the basis of VSS theory. On the other hand, the fast controller, which is robust against the unstructured uncertainty, is designed for the fast model on the basis of H∞ control theory. Then the composite control is constructed with the slow controller and the fast controller. Some results of numerical simulations are presented to show the effectiveness of this design procedure.

Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.


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