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Volume 19 (2022)
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Advances in Computer Science and Engineering
Advances in Computer Science and Engineering
Volume 2, Issue 3, Pages 201 - 219 (November 2008)
FINE GAIN TUNING FOR MODEL-BASED ROBOTIC SERVO CONTROLLERS USING GENETIC ALGORITHMS
Fusaomi Nagata (Japan) and Keigo Watanabe (Japan)
Abstract:
Resolved acceleration control method and computed torque control method are used for nonlinear control of industrial manipulators, which are composed of a model base portion and a servo portion. The servo portion is a close loop system with respect to the position and velocity. On the other hand, the model base portion has the inertia term, gravity term and centrifugal/Coriolis term, which work for canceling the nonlinearity of manipulator. In order to realize high control stability, the position and velocity feedback gains used in the servo portion should be selected suitably. In this paper, a simple but effective fine tuning method after the manual tuning process is introduced for the position and velocity feedback gains in the servo portion. At the first step, the search space for the gains are roughly narrowed down by a controller designer, e.g., considering the critically damped condition. At the next step, the gains are finely tuned within the space by using genetic algorithms. The genetic algorithms search for the better combination of the position and velocity gains. Simulations are conducted using a dynamic model of PUMA560 manipulator to validate the effectiveness of the proposed method.
Keywords and phrases:
model-based servo controller, fine gain tuning, genetic algorithms, PUMA560 manipulator, critically damped condition.
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