Cross-coupled Iterative Learning Control Research Articles

Cross-coupled iterative learning control: A computationally efficient approach applied to an industrial flatbed printer

Cross-coupled iterative learning control (ILC) can improve the contour tracking performance of manufacturing systems significantly. This paper aims to develop a framework for norm-optimal cross-coupled ILC that enables intuitive tuning of time- and iteration-varying weights of the exact contour error and its tangential counterpart. This leads to an iteration-varying ILC algorithm for which convergence conditions are developed. In addition, a resource-efficient implementation is developed that reduces the computational load significantly and enables the use of long reference signals. The approach is experimentally validated on an industrial flatbed printer.

Task Space Contouring Error Estimation and Precision Iterative Control of Robotic Manipulators

The task space contouring performance is significant for the machining accuracy of industrial robotic manipulators, but the contouring control of end-effector which is important in the industry has received scant attention. In this letter, a novel task space contouring error estimation and control scheme is developed to address the end-effector precision contouring problem of robotic manipulators. In order to quantify manipulator contouring performance, a 6-DOF synchronized contouring error is defined in the task space. Based on the definition, an optimization problem is constructed and solved to achieve high precision contouring error estimation. The proposed method shows excellent estimation accuracy even under extreme contouring conditions, which is the prerequisite for accurate contouring control. Then the solved contouring error is iteratively compensated into the end-effector reference in a feedforward way to achieve contouring error reduction. The effectiveness of the proposed method is derived theoretically under the assumption of small error and nonsingularity. Comparative experiments are conducted on an industrial manipulator. Quantitative results on different extreme trajectories demonstrate that the proposed control scheme not only has advantages in control stability and implementation simplicity, but also outperforms traditional joint space iterative learning control and cross-coupled iterative learning control on the end-effector contouring accuracy. The proposed method actually provides a practical solution to high-precision contouring applications for industrial manipulators.

Non-linear contour tracking using feedback PID and feedforward position domain cross-coupled iterative learning control

In this paper, a position domain cross-coupled iterative learning controller combining proportional–integral–derivative (PID)-type iterative learning control (ILC) and proportional–derivative (PD)-type cross-coupling control (CCC) is presented aiming at non-linear contour tracking in multi-axis motion systems. Traditional individual control methods in the time domain suffer from poor synchronization of relevant motion axes. The complicated computation of coupling gains in CCC and cross-coupled ILC (CCILC) restricts their applications for non-linear contour. The proposed position domain CCILC (PDCCILC) approach introduces a position domain design concept into CCILC to improve synchronization and performance for non-linear contour tracking and it relies less on the accuracy of coupling gains than conventional CCILC. The stability and performance analysis are conducted using a lifted system representation. The contour error vector method is applied to estimate the coupling gains in simulations and experiments. Simulation and experimental results of three typical non-linear contour tracking cases (i.e. semi-circle, parabola and spiral) based on a two-axis micro-motion stage demonstrate superiority and efficacy of the proposed feedback PID and feedforward PDCCILC compared with existing ILC and CCILC in the time domain.

Contour accuracy improvement of a flexure-based micro-motion stage for tracking repetitive trajectory

Flexure-based micro-motion mechanisms have been widely utilized in modern precision industry due to their inherent merits, while model uncertainty, uncertain nonlinearity, and cross-coupling effect will obviously deteriorate their contour accuracy, especially in the high-speed application. This paper aims at improving the contouring performance of a flexure-based micro-motion stage utilized for tracking repetitive trajectories. The dynamic characteristic of the micro-motion stage is first studied and modeled as a second-order system, which is identified through an open-loop sinusoidal sweeping test. Then the iterative learning control (ILC) scheme is utilized to improve the tracking performance of individual axis of the stage. A nonlinear cross-coupled iterative learning control (CCILC) scheme is proposed to reduce the coupling effect among each axis, and thus improves contour accuracy of the stage. The nonlinear gain function incorporated into the CCILC controller can effectively avoid amplifying the non-recurring disturbances and noises in the iterations, which can further improve the stage’s contour accuracy in high-speed motion. Comparative experiments between traditional PID, ILC, ILC & CCILC, and the proposed ILC & nonlinear CCILC are carried out on the micro-motion stage to track circular and square trajectories. The results demonstrate that the proposed control scheme outperforms other control schemes much in improving the stage’s contour accuracy in high-speed motion. The study in this paper provides a practically effective technique for the flexure-based micro-motion stage in high-speed contouring motion.

Cross-coupled iterative learning control of systems with dissimilar dynamics: design and implementation

Cross-coupled iterative learning control has previously been applied to contour tracking problems with planar manufacturing robots in which both axes can be characterised as similar systems; having similar dynamics and identical hardware. However, there are many repetitive applications in which dynamically dissimilar systems cooperate to pursue a primary performance objective. This article introduces a novel framework to couple dynamically dissimilar systems while applying iterative learning control, showing the ability to noncausally compensate for a slow system with a fast system. In this framework, performance requirements for a primary objective can more readily be achieved by emphasising an underutilised fast system instead of straining a less-capable slow system. The controller is applied to a micro-robotic deposition manufacturing system to coordinate a slow extrusion system axis and a fast positioning system axis to pursue the primary performance objective, dimensional accuracy of a fabricated part. Experimental results show a 14% improvement in fabrication-dimensional accuracy with only marginal changes in actuator effort, as compared to independently controlled axes.

A Cross-Coupled Iterative Learning Control Design for Precision Motion Control

This paper presents an improved method for precision motion control by combining individual axis iterative learning control (ILC) and cross-coupled ILC (CCILC) into a single control input. CCILC is a new method in which a multi-axis cross-coupled controller (CCC) is reformatted into a single-input single-output (SISO) ILC format. Applying the techniques of ILC to CCC enables learning of the cross-coupled error which leads to a modified control signal and subsequent improvements in the contour trajectory tracking performance. In this paper, performance of the combined ILC and CCILC system is compared to standard feedback control through computer simulations and experimental testing on a Cartesian robotic system. Sufficient stability and convergence properties for the combined system are presented along with a modified approach for determining monotonic convergence of systems that are computationally challenging. The combined design is shown to enhance the precision motion control of the robotic system through performance improvements in individual axis tracking and contour tracking.