Performance Enhancement for Rigid Tapping by Iterative Learning Control

Self Cite

This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.

Section: Ilc For Robotmentioning

confidence: 99%

“…To guarantee monotonic convergence, the learning gain matrix

normalϕ

is designed such that

Q = I - ΠΦ

Q goodbreak= {[\begin{matrix} center \\ I - CB ϕ_{0, 1} & 0 & \dots & 0 \\ - {false\sum}_{i = 0}^{1} {italicCA}^{1 - i} Bϕ {}_{i, 1} & I - CB ϕ_{1, 2} & \dots & 0 \\ ⋮ & ⋮ & ⋱ & ⋮ \\ - {false\sum}_{i = 0}^{N - 1} {italicCA}^{N - i - 1} Bϕ {}_{i, 1} & - {false\sum}_{i = 0}^{N - 1} {italicCA}^{N - i - 1} Bϕ {}_{i, 2} & \dots & I - CB ϕ_{N - 1, N} \end{matrix}]}_{6 N \times 6 N}

which is the block lower triangular.…”

Section: Design Of Ilcmentioning

confidence: 99%

Iterative learning control for integrated system of robot and machine tool

Chen

2022

Self Cite

“…Shi et al [25] developed a model predictive contouring control for biaxial motion systems to achieve an accurate contour tracking performance. Chen et al [26] developed an iterative learning control to reduce synchronous errors in the z ‐axis and spindle motions during the rigid tapping process in computer numerical control (CNC) machine tools. Thus, the PMSM control design must consider the expandability of advanced motion control applications using multiple PMSMs.…”

Section: Introductionmentioning

confidence: 99%

Development of an extended proportional–integral–proportional control for improving positional motion performance of permanent magnet synchronous motor‐driven servomechanism

Yeh

Chang

2022

To achieve high‐speed and high‐accuracy positional motions in a permanent magnet synchronous motor (PMSM)‐driven servomechanism, this study developed an extended proportional–integral–proportional (PIP) control, particularly by considering the limitations of the hardware structure of commercialized PMSM drivers. PIP feedback and feedforward controls were developed, such that the existing PIP feedback control, which is extensively used in commercialized PMSM drivers, can perform dynamic and friction torque compensations in PMSMs. Thereafter, an extended PIP control with the newly added design degrees of freedom was developed based on the PIP feedback and feedforward control structures, thereby further improving the position response of the PMSM‐driven servomechanism and positional motion performance of advanced motion control systems. Position control experiments were performed on a commercialized PMSM pack installed on the rotary table of a five‐axis computer numerical control (CNC) machining center. Results indicate that the extended PIP control can significantly reduce position errors, which can be minimally influenced by the change in frequency of the position command. Furthermore, circular motion experiments were performed along the motion axes of the five‐axis CNC machining center. Results indicate that the extended PIP control with dynamic matching design can significantly reduce the root‐mean‐square value of contouring errors and roundness error value. Experimental results validate the feasibility of the extended PIP control for improving the positional motion performance of the PMSM‐driven servomechanism.

“…Koren initially proposed cross‐coupled control (CCC) [10]. Although several CCC‐based contouring controllers, for example, generalized cross‐coupled controller [11], CCC with frequency modulated interpolation [12], and iterative learning control [13], were designed to yield higher contouring performance, most of them were carried out for only two‐ or three‐axis Cartesian motion systems.…”

Section: Introductionmentioning

confidence: 99%

MPC‐based robust contouring control for a robotic machining system

Kornmaneesang

Chen

2020

Self Cite

Recently, robot manipulators have been adopted to perform machining tasks, instead of only automation tasks. The key machining task is to follow a desired contour. Therefore, the machining accuracy can be improved by reducing contour error. However, only a few researches concentrate on solving the contouring problem in the robotic machining system. In this paper, a model predictive control (MPC)‐based contouring control scheme is proposed for a machining system based on a 5‐DOF dual‐arm robot. The contouring control problem is transformed into the regulation problem by using the method of equivalent errors. An MPC algorithm is developed to minimize the contour error by optimizing a sequence of control actions under torque constraints. In addition, robustness is upgraded by integral sliding mode control (ISMC). Experiments are carried out for the proposed methods and two other conventional methods. The results show that the proposed control scheme can achieve much better contouring performance than the conventional methods, indicating the effectiveness of the proposed controller.