Simulation of Fine Gain Tuning Using Genetic Algorithms for Model-Based Robotic Servo Controllers

Abstract

Resolved acceleration control method or computed torque method is used for nonlinear control of industrial manipulators, which is composed of a model base portion and a servo portion. The servo portion is a close loop 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 gains used in the servo portion should be selected suitably. In this paper, a simple but effective fine tuning method after manual tuning is introduced for the position and velocity feedback gains in the servo portion. At the first step, base values of the gains are roughly selected by a controller designer, e.g., considering the critically damped condition. After that, the base values are finely tuned by genetic algorithms. 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.