Expert Skill-Based Gain Tuning in Discrete-Time Adaptive Control for Robots

This paper presents a gain tuning method according to the sampling period in discrete-time adaptive control for robots. Gain matrices of model-based adaptive control in a continuous-time system are allowed high gain positive definite. However, the maximum of the gains depends on the sampling time, and gain tuning is a very time consuming work. Therefore, it is desirable to give an insight of gain tuning in discrete-time adaptive control. The proposed gain tuning consists of two steps. The first step is a gain tuning at the basic sampling time by a skillful specialist by means of trial and error. The second step that is executed if the sampling period changes, is a new gain calculation based on a new sampling period. The simulation and experiments of 1-dof robot and 3-dof robot show that the proposed gain tuned controller is stable at the large variance of the sampling period changes and more accurate than the fixed gain controller.