A Task-Level Iterative Learning Control Algorithm for Accurate Tracking in Manipulators With Modeling Errors and Stringent Joint Position Limits

2016 ◽  
Author(s):  
Pranav A. Bhounsule ◽  
Abhishek A. Bapat ◽  
Katsu Yamane
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Joint Position ◽  
Task Space ◽  
Learning Gain ◽  
End Effector ◽  
Modeling Errors

We present an iterative learning control algorithm for accurate task space tracking of kinematically redundant robots with stringent joint position limits and kinematic modeling errors. The iterative learning control update rule is in the task space and consists of adding a correction to the desired end-effector pose based on the tracking error. The new desired end-effector pose is then fed to an inverse kinematics solver that uses the redundancy of the robot to compute feasible joint positions. We discuss the stability, the rate of convergence and the sensitivity to learning gain for our algorithm using quasi-static motion examples. The efficacy of the algorithm is demonstrated on a simulated four link manipulator with joint position limits that learns the modeling error to draw the figure eight in 4 trials.

Accurate Task-Space Tracking for Humanoids with Modeling Errors Using Iterative Learning Control

2017 ◽  
Vol 14 (03) ◽  
pp. 1750015 ◽  
Author(s):  
Pranav A. Bhounsule ◽  
Katsu Yamane
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Complex Structure ◽  
Tracking Error ◽  
Kinematic Model ◽  
Type Systems ◽  
Learning Control ◽  
Iterative Learning ◽  
Task Space ◽  
Modeling Errors

Precise task-space tracking with manipulator-type systems requires an accurate kinematic model. In contrast to traditional manipulators, sometimes it is difficult to obtain an accurate kinematic model of humanoid robots due to complex structure and link flexibility. Also, prolonged use of the robot will lead to some parts wearing out or being replaced with a slightly different alignment, thus throwing off the initial calibration. Therefore, there is a need to develop a control algorithm that can compensate for the modeling errors and quickly retune itself, if needed, taking into account the controller bandwidth limitations and high dimensionality of the system. In this paper, we develop an iterative learning control algorithm that can work with existing inverse kinematics solvers to refine the joint-level control commands to enable precise tracking in the task space. We demonstrate the efficacy of the algorithm on a theme-park-type humanoid doing a drawing task, serving drink in a glass, and serving a drink placed on a tray without spilling. The iterative learning control algorithm is able to reduce the tracking error by at least two orders of magnitude in less than 20 trials.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

scholarly journals A Design of Parameter Optimal Iterative Learning Control for Linear Discrete-Time Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wataru Kase
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Gain ◽  
Quadratic Cost ◽  
Discrete Time Systems ◽  
Monotonic Convergence ◽  
Time Systems ◽  
Quadratic Cost Function

The iterative learning control algorithm proposed by Owens and Feng, which guarantees the monotonic convergence of the tracking error norms along with the trial, will be modified. The learning gain of the proposed method will be determined through a quadratic cost function. MIMO plant case will be also discussed. Numerical simulations will be presented to confirm the validity of the proposed design.

Iterative Learning Control for a Class of Time-Delay Systems with Time-Varying Uncertainties

2013 ◽  
Vol 310 ◽  
pp. 428-434
Author(s):  
Ye Lei Zhao
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Delay Systems ◽  
Time Varying ◽  
Adaptive Iterative Learning Control ◽  
Control Approach

An adaptive iterative learning control algorithm is proposed for a class of known time-delay nonlinear system with unknown time-varying parameter. A parameter separation technique is used to deal with time-delay problem. The control approach presented in this paper can guarantee that the tracking error converges to zero uniformly on the iteration interval as the iteration number approaches to infinity. A simulation example is provided to illustrate the efficiency of the proposed control algorithm.

scholarly journals A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

2016 ◽  
Vol 26 (3) ◽  
pp. 297-310 ◽  
Author(s):  
Meng Wang ◽  
Guangrong Bian ◽  
Hongsheng Li
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Repetitive Tasks ◽  
Initial Control ◽  
Self Tuning ◽  
Tracking Trajectory

Abstract This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

scholarly journals New Iterative Learning Control Algorithm Using Learning Gain Based on σ Inversion for Nonsquare Multi-Input Multi-Output Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Leila Noueili ◽  
Wassila Chagra ◽  
Moufida Ksouri
Keyword(s):  
Iterative Learning Control ◽  
Mimo Systems ◽  
Linear Time ◽  
Tracking Error ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Learning Gain ◽  
Model Inversion ◽  
Direct Model

Model inversion Iterative Learning Control (ILC) for a class of nonsquare linear time variant/invariant multi-input multi-output (MIMO) systems is considered in this paper. A new ILC algorithm is developed based on σ-right inversion of nonsquare learning gain matrices to resolve the matrix inversion problems appeared in the direct model inversion of nonsquare MIMO systems. Furthermore, a sufficient and necessary monotonic convergence condition is established. With rigorous analysis, the proposed ILC scheme guarantees the convergence of the tracking error. To prove the effectiveness and to illustrate the performance of the proposed approach for linear time-invariant (LTI) and time-varying nonsquare systems, two illustrative examples are simulated.

scholarly journals An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saleem Riaz ◽  
Hui Lin ◽  
Muhammad Waqas ◽  
Farkhanda Afzal ◽  
Kai Wang ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Position Control ◽  
Tracking Error ◽  
Learning Control ◽  
Convergence Condition ◽  
Design Criterion ◽  
Iterative Learning ◽  
Current Error ◽  
P Type

Traditional and typical iterative learning control algorithm shows that the convergence rate of error is very low for a class of regular linear systems. A fast iterative learning control algorithm is designed to deal with this problem in this paper. The algorithm is based on the traditional P-type iterative learning control law, which increases the composition of adjacent two overlapping quantities, the tracking error of previous cycle difference signals, and the current error difference. Using convolution to promote Young inequalities proved strictly that, in terms of Lebesgue-p norm, when the number of iterations tends to infinity, the tracking error converges to zero in the system and presents the convergence condition of the algorithm. Compared with the traditional P-type iterative learning control algorithm, the proposed algorithm improves convergence speed and evades the defect using the norm metric’s tracking error. Finally, the validation of the effectiveness of the proposed algorithm is further proved by simulation results.

scholarly journals Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 152
Author(s):  
Dongqi Ma ◽  
Hui Lin
Keyword(s):  
Permanent Magnet ◽  
Iterative Learning Control ◽  
Mathematical Proof ◽  
Permanent Magnet Synchronous Motor ◽  
Learning Control ◽  
Synchronous Motor ◽  
Iterative Learning ◽  
Learning Gain ◽  
Periodic Load ◽  
P Type

To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.

Sign in / Sign up

Export Citation Format

Share Document