Iterative Learning Control with Desired Gravity Compensation under Saturation for a Robotic Machining Manipulator

Abstract

This paper proposes the design of a hybrid iterative learning controller for a four-degree-of-freedom (DOF) robotic machining manipulator (RMM). It combines a nonlinear saturated (sat) proportional + integral + derivative (PID) control with desired gravity compensation (dgc) and proportional + derivative-(PD-) based iterative learning control (ILC). The sat(PID) control is the primary component that maintains the local stability of the entire RMM system and the PDILC component provides robustness to parameter variations and uncertainties in the robot dynamics. Global asymptotic stability of the proposed control algorithm is conducted using Lyapunov direct method and LaSalles invariance principle. Simulation results show the effectiveness and robustness of the proposed hybrid iterative learning controller. It is also shown that the proposed controller achieved better tracking performances compared to conventional sat(PDdgc) feedback controller.

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.…”

On the Development of Learning Control for Robotic Manipulators

“…The control system is tested by a PUMA 6-DOF serial mechanism with revolute joints. In [42], a hybrid iterative learning control system for a 4-DOF robot mechanism that is used in CNC machine tools is developed. The control system consists of a nonlinear saturated PID with desired gravity compensation and PD-based iterative learning control.…”

Design of Adaptive Switching Controller for Robotic Manipulators with Disturbance

“…In this section, a sufficient condition is proposed to ensure the system is stable without knowing the supremum of bounded disturbance. (15), (16), (17), and (18) can guarantee system (4) to obtain global asymptotic stability:…”

“…The excellent performance of tracking can be considered as one of the most important requirements of robotic manipulator because of its highly nonlinear, multivariable, strongly coupling, and time-varying systems. Many schemes were presented in the past years [1][2][3][4]. Generally speaking, in the process of operating robotic manipulators, there are many uncertainties and disturbances, such as the nonlinear friction and variational payloads.…”

“…The presented adaptive law iṡ The adaptive laws (7) make the estimation parameters change when the corresponding subsystem is active, which can avoid the coupling of different estimation vectors. For robotic system(4) and the adaptive switching controller(5),(7) can guarantee the global convergence of the tracking error. That is, lim →∞ ( ) = 0.Proof.…”