New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.