Genetic algorithms with variable search space function for fine gain tuning of model-based robotic servo controller Online publication date: Sat, 12-Jul-2014
by Akimasa Otsuka; Akihiko Ikeda; Fusaomi Nagata
International Journal of Mechatronics and Manufacturing Systems (IJMMS), Vol. 6, No. 1, 2013
Abstract: In this paper, genetic algorithms with a variable search space function are proposed for fine gain tuning of a resolved acceleration controller which is one of model-based robotic servo controllers. To realise a stable controller, position and velocity feedback gains should be tuned suitably. Proposed genetic algorithms temporally vary the search space if a certain condition is satisfied. The variable search space function is activated in genetic algorithms if the optimal solution is not updated for fixed generations. When the function is active, an update within the fixed generations or non-updated situation for the fixed generations makes the function terminated. The proposed method is evaluated through a trajectory following control problem, in which it is tried to search better feedback gains more rapidly. Simulations are conducted by using the dynamic model of PUMA560 manipulator. The results demonstrate the effectiveness and the promise of the proposed method.
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