Iterative learning control with sampled-data feedback for robot manipulators

Abstract

This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.

Calibration of UR10 Robot Controller through Simple Auto-Tuning Approach

“…where T s is the sampling period, chosen such that T m = NT s . Using now (16) and (17), the next result holds: y c − jy s = −j0.5T m A y (cosϕ y + jsinϕ y ) = −j0.5T m A y e jϕ y…”

“…The most challenging step in employing PID controllers, for non-experts in control engineering, is the process of parameter tuning. Nowadays, the self-tuning PID digital controller provides much convenience in engineering [15,16]. Optimal control of a plant (in this particular application, the robot arm) is highly dependent on the plant behavior.…”

A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators

“…As a side note, learning control of robotics is quickly developed after the development of adaptive control of robotic manipulators. This is mainly used to address the problem of joint friction and other uncertainties [22][23][24][25][26][27][28][29][30][31][32][33]. We will not elaborate on each of the references cited.…”

“…In [25], the use of approximants in the implementation of repetitive learning controls for the asymptotic joint position tracking of robots with uncertain dynamics was presented. In [27], the authors incorporated an off-line model based nonlinear iterative learning control to improve the stability of sampled-data feedback control for robotics. In [29], a control system was developed by combining the model-based adaptive control, repetitive learning control and PD control in which the model-based adaptive control input dominates over the other inputs.…”

On the Development of Learning Control for Robotic Manipulators

“…However, the control structure is quite complex and also the control parameters contain a sign function. In [44], the authors investigate nonlinear iterative learning control with sampled-data feedback for robotic mechanisms. This control system can be used in a robotic mechanism that has more than 6-DOF due to the existence of a sampled-data feedback system.…”