An Energy-Optimal Reference Trajectory with Iterative Learning-Based Control

2021 ◽  
Author(s):  
Tong Zhou ◽  
Hui Li ◽  
Xuewei Xiang
Keyword(s):  
Iterative Learning ◽  
Reference Trajectory

scholarly journals Reference Trajectory Modification Based on Spatial Iterative Learning for Contour Control of Two-Axis NC Systems

2020 ◽  
Vol 25 (3) ◽  
pp. 1266-1275
Author(s):  
Jiangang Li ◽  
Yiming Wang ◽  
Yanan Li ◽  
Wenshu Luo
Keyword(s):  
Iterative Learning ◽  
Reference Trajectory ◽  
Contour Control

scholarly journals Iterative learning of human partner’s desired trajectory for proactive human–robot collaboration

2020 ◽  
Vol 4 (2) ◽  
pp. 229-242
Author(s):  
Jingkang Xia ◽  
Deqing Huang ◽  
Yanan Li ◽  
Na Qin
Keyword(s):  
Learning Algorithm ◽  
Interaction Force ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Robot Controller ◽  
Task Period ◽  
Human Partner ◽  
Control Scheme ◽  
Control Objective ◽  
Human Robot Collaboration

Abstract A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration.

Analysis of iterative learning control for an oscillating control system with two delays

2017 ◽  
Vol 40 (6) ◽  
pp. 1757-1765 ◽  
Author(s):  
Chengbin Liang ◽  
JinRong Wang
Keyword(s):  
Control System ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Reference Trajectory ◽  
Output State ◽  
Control Functions ◽  
Input Control ◽  
Two Delays

In order to track the desired reference trajectory from an oscillating control system with two delays in a finite time interval, we design iterative learning control updating laws to generate a sequence of input control functions such that the error between the output and the desired reference trajectories tends to zero via a suitable norm in the sense of uniform convergence. Here, we adopt a delayed matrix function to characterize the output state, which can be easily solved in the simulation. As a result, convergence analysis results are given. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.

scholarly journals Iterative learning contouring controller based on trajectory generation with linearly interpolated contour error estimation and Bézier reposition trajectory for computerized numerical control machine tool feed drive systems

2019 ◽  
Vol 11 (8) ◽  
pp. 168781401986810
Author(s):  
Yogi Muldani Hendrawan ◽  
Kenneth Renny Simba ◽  
Naoki Uchiyama
Keyword(s):  
Error Compensation ◽  
Iterative Learning ◽  
Numerical Control ◽  
Contour Error ◽  
Reference Trajectory ◽  
Control Machine Tool ◽  
Mechatronic Systems ◽  
Computerized Numerical Control ◽  
Mechanical Components ◽  
Control Machine

In industrial applications, highly accurate mechanical components are generally required to produce advanced mechanical and mechatronic systems. In machining mechanical components, contour error represents the product shape quality directly, and therefore it must be considered in controller design. Although most existing contouring controllers are based on feedback control and estimated contour error, it is generally difficult to replace the feedback controller in commercial computerized numerical control machines. This article proposes an embedded iterative learning contouring controller by considering the linearly interpolated contour error compensation and Bézier reposition trajectory, which can be applied in computerized numerical control machines currently in use without any modification of their original feedback controllers. While the linearly interpolated contour error compensation enhances tracking performance by compensating the reference input with an actual value, the Bézier reposition trajectory enables smooth velocity transitions between discrete points in the reference trajectory. For performance analysis, the proposed controller was implemented in a commercial three-axis computerized numerical control machine and several experiments were conducted based on typical three-dimensional sharp-corner and half-circular trajectories. Experimental results showed that the proposed controller could reduce the maximum and mean contour errors by 45.11% and 54.48% on average, compared to embedded iterative learning contouring controller with estimated contour error. By comparing to embedded iterative learning contouring controller with linearly interpolated contour error compensation, the maximum and mean contour errors are reduced to 20.54% and 26.92%, respectively.

A New Nonlinear Iterative Learning Controller for Road Simulator

2013 ◽  
Vol 373-375 ◽  
pp. 1546-1550 ◽  
Author(s):  
Gang Xu ◽  
Jun Yu Zhou ◽  
Hong Zhou
Keyword(s):  
Nonlinear System ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Numerical Optimization ◽  
State Space Model ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Control Scheme ◽  
Multiple Reference ◽  
Nonlinear State

A new model based nonlinear iterative learning controller to replicate multiple reference trajectory is presented. The approach consists of a constrained Gauss-Newton numerical optimization algorithm, which is able to inverse a nonlinear system accurately. It uses the moving horizon idea from MPC to improve the computational efficiency. Combined with a class of nonlinear state-space model and an existing nonlinear iterative learning control scheme, this allows accurate tracking control of a nonlinear system. Numerical results are used to illustrate the algorithm.

Anti-disturbance iterative learning tracking control for space manipulators with repetitive reference trajectory

2019 ◽  
Vol 39 (3) ◽  
pp. 401-409 ◽  
Author(s):  
Jian Zhong Qiao ◽  
Hao Wu ◽  
Yukai Zhu ◽  
Jianwei Xu ◽  
Wenshuo Li
Keyword(s):  
Tracking Control ◽  
Hybrid Control ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Tracking Accuracy ◽  
Model Uncertainties ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Space Manipulators ◽  
Accuracy And Repeatability

Purpose This paper is concerned with the repetitive trajectory tracking control for space manipulators under model uncertainties and vibration disturbances. Design/methodology/approach The model uncertainties and link vibration of manipulators will degrade the tracking performance of space manipulators; in this paper, a new hybrid control scheme that consists of a composite hierarchical anti-disturbance controller and an iterative learning controller is developed to solve this problem. Findings The composite hierarchical controller can effectively attenuate model uncertainties and reject vibration disturbances, whereas the iterative learning controller is able to improve the tracking accuracy for repetitive reference trajectory. Originality/value The proposed scheme compensates for the shortcomings of iterative learning control which can only deal with repetitive disturbances, ensuring the accuracy and repeatability of space manipulators under model uncertainties and random disturbances.

Current cycle feedback iterative learning control for tracking control of magnetic levitation system

2019 ◽  
Vol 42 (3) ◽  
pp. 543-550
Author(s):  
Vimala Kumari Jonnalagadda ◽  
Vinodh Kumar Elumalai ◽  
Shantanu Agrawal
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Magnetic Levitation ◽  
Learning Control ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Maglev System ◽  
Levitation System ◽  
Current Cycle ◽  
Magnetic Levitation System

This paper presents the current cycle feedback iterative learning control (CCF-ILC) augmented with the modified proportional integral derivative (PID) controller to improve the trajectory tracking and robustness of magnetic levitation (maglev) system. Motivated by the need to enhance the point to point control of maglev technology, which is widely used in several industrial applications ranging from photolithography to vibration control, we present a novel CCF-ILC framework using plant inversion technique. Modulating the control signal based on the current tracking error, CCF-ILC reduces the dependency on accurate plant model and significantly improves the robustness of the closed loop system by synthesizing the causal filters to counteract the effect of model uncertainty. To assess the stability, we present a maximum singular value based criterion for asymptotic stability of linear iterative system controlled using CCF-ILC. In addition, we prove the monotonic convergence of output sequence in the neighbourhood of reference trajectory. Finally, the proposed control framework is experimentally validated on a benchmark magnetic levitation system through hardware in loop (HIL) testing. Experimental results substantiate that synthesizing CCF-ILC with the feedback controller can significantly improve the trajectory tracking and robustness characteristics of maglev system.

Consensus Tracking by Iterative Learning Control for Linear Heterogeneous Multiagent Systems Based on Fractional-Power Error Signals

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 185 ◽  
Author(s):  
Yu-Juan Luo ◽  
Cheng-Lin Liu ◽  
Guang-Ye Liu
Keyword(s):  
Multiagent Systems ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Fractional Power ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Order Tracking ◽  
Consensus Tracking

This paper deals with the consensus tracking problem of heterogeneous linear multiagent systems under the repeatable operation environment, and adopts a proportional differential (PD)-type iterative learning control (ILC) algorithm based on the fractional-power tracking error. According to graph theory and operator theory, convergence condition is obtained for the systems under the interconnection topology that contains a spanning tree rooted at the reference trajectory named as the leader. Our algorithm based on fractional-power tracking error achieves a faster convergence rate than the usual PD-type ILC algorithm based on the integer-order tracking error. Simulation examples illustrate the correctness of our proposed algorithm.

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