A Moderate Online Servo Controller Parameter Self-Tuning Method via Variable-Period Inertia Identification

As universal servos are economical, they are chosen for most of the industrial applications. However, tuning them accurately poses some challenges. Accurate electrical and mechanical parameters are essential for a model-based high-performance servo controller design. In motion control, the variation of inertia is much more significant than that of other parameters. Thus, inertia identification is a key to effective online controller parameter self-tuning, but most conventional inertia-identification methods cannot be well applied in some complicated situations, such as those caused by irregularly and slowly varying speed. Additionally, the traditional control parameter tuning theory displays a deep understanding of the relationship between controller gain and inertia, but lacks an analysis of the maximum bandwidth of the system. In this paper, based on a widely accepted structure that is composed of inertia identification and online controller self-tuning, several simple but useful modifications are proposed. First, to reduce the noise from an encoder quantization error and thus to improve the accuracy of inertia identification, a motor acceleration calculation method featuring an inconstant period is proposed. Then, the scope of the application is extended to make it suitable for position control by redesigning the inertia updating time of the conventional method. In addition, to guarantee the stability of servo systems, the upper constraint of the expected maximum system bandwidth is derived by taking the controller saturation nonlinearity and hardware capacity into consideration. Finally, a modified moderate systematic online servo controller parameter self-tuning method via variable-period inertia identification is presented. The validity, effectiveness, and advantages of proposed strategies are verified by several experimental results.