High performance swing velocity tracking control of hydraulic excavators

Author(s):  
Bin Yao ◽  
Jiao Zhang ◽  
D. Koehler ◽  
J. Litherland
Author(s):  
J. Rastegar ◽  
L. Liu ◽  
M. Mattice

Abstract An optimal simultaneous kinematic, dynamic and control design approach is proposed for high performance computer controlled machines such as robot manipulators. The approach is based on the Trajectory Pattern Method (TPM) and a fundamentally new design philosophy that such machines in general and ultra-high performance machines in particular must only be designed to perform a class or classes of motions effectively. In the proposed approach, given the structure of the manipulator, its kinematic, dynamic and control parameters are optimized simultaneously with the parameters that describe the selected trajectory pattern. In the example presented in this paper, a weighted sum of the norms of the higher harmonics appearing in the actuating torques and the integral of the position and velocity tracking errors are used to form the optimality criterion. The selected optimality criterion should yield a system that is optimally designed to accurately follow the specified trajectory at high speed. Other objective functions can be readily formulated to synthesize systems for optimal performance. The potentials of the developed method and its implementation for generally defined motion patterns are discussed.


2001 ◽  
Vol 9 (4) ◽  
pp. 645-653 ◽  
Author(s):  
M. Feemster ◽  
P. Aquino ◽  
D.M. Dawson ◽  
A. Behal

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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