Application of Genetic Algorithms to Fine-gain Tuning of Improved the Resolved Acceleration Controller

Position and velocity feedback gains should be tuned suitably to realize a stable position controller. We already proposed genetic algorithms with variable search spaces, which successfully address this problem. The effectiveness was proved through fine-gain tuning of a resolved acceleration controller, which is one of the model-based robotic servo controllers. Additionally, the viscous and Coulomb frictions of the joints were considered in the dynamic model of the manipulator, to realize a more realistic robotic simulation. As a result, undesirable oscillations caused by the friction were observed because of the nonlinearity of the friction model. In this paper, we add a low-pass filter to the velocity term in the resolved acceleration controller to reduce the influence of the friction. The improved controller is evaluated through fine-gain tuning for a trajectory-following control problem. Simulations are conducted by using the dynamic model of a PUMA560 manipulator. The results demonstrate the effectiveness of the improved controller.