Improved Cross-Coupled Iterative Learning Control for Contouring NURBS Curves

2018 ◽  
Author(s):  
Ashley Armstrong ◽  
Amy Wagoner Johnson ◽  
Andrew Alleyne
Keyword(s):  
Iterative Learning Control ◽  
Computer Aided Design ◽  
Cost Effective ◽  
Learning Control ◽  
Current Control ◽  
Iterative Learning ◽  
Free Form ◽  
Reference Trajectory ◽  
Layer By Layer ◽  
Performance Improvements

Additive manufacturing (AM) uses computer-aided design to construct parts layer by layer. Relative to traditional manufacturing processes, AM provides a time-efficient and cost-effective way to produce low-volume, customized parts with complex geometries. This work presents an improved Cross-Coupled iterative learning control (CCILC) scheme to overcome current limitations in contour following for complex, free-form curves in AM. The approach involves modifying the definition of the error vector used in the individual axis iterative learning controllers and defining time varying weightings based on the curvature of the reference trajectory to couple tracking and contour errors. In this paper, the design for the improved CCILC system is presented, and the performance of this system is compared to the performance of existing ILC control schemes via simulations. In comparison to the current control methods, the simulation results demonstrate significant performance improvements for contour tracking of a reference trajectory with high levels of curvature.

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.

Spatial Iterative Learning Control for Multi-material Three-Dimensional Structures

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Zahra Afkhami ◽  
Christopher Pannier ◽  
Leontine Aarnoudse ◽  
David Hoelzle ◽  
Kira Barton
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Single Layer ◽  
Learning Control ◽  
Iterative Learning ◽  
Layer By Layer ◽  
Additive Process ◽  
Jet Printing

Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process.

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.

Data-Weighting Based Discrete-Time Adaptive Iterative Learning Control for Nonsector Nonlinear Systems With Iteration-Varying Trajectory and Random Initial Condition

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Ronghu Chi ◽  
Zhongsheng Hou ◽  
Shangtai Jin
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Recursive Least Squares ◽  
Reference Trajectory ◽  
Random Initial Condition ◽  
Adaptive Iterative Learning Control ◽  
Linear Growth Condition

In this paper, a new discrete-time adaptive iterative learning control (AILC) approach is presented to deal with nonsector nonlinearities by incorporating a recursive least-squares algorithm with a nonlinear data weighted coefficient. This scheme is also extended as a d-iteration-ahead adaptive iterative learning predictive control to address for multiple inputs multiple outputs (MIMO) nonlinear systems with unknown input gains. A major distinct feature of the presented methods is that the global stability result is obtained through Lyapunov analysis without assuming any linear growth condition on the nonlinearities. Another distinct feature is that the pointwise convergence of the presented methods is achieved over a finite interval without requiring any identical conditions on the initial states and reference trajectory.

scholarly journals Adaptive fuzzy iterative learning control of robotic systems with time-delay outputs and input dead zone via observer

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1571-1580
Author(s):  
Youan Zhang ◽  
Jianming Wei ◽  
Hong Wang ◽  
Jingmao Liu
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Dead Zone ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Approximation Technique ◽  
Reference Trajectory ◽  
Adaptive Fuzzy ◽  
Output Tracking

This paper presents an adaptive fuzzy iterative learning control method for the output tracking problem of robotic systems with unknown time delay output and input dead-zone. A state observer is designed to estimate unmeasurable velocity variables. By introducing boundary layer function, the identical initial condition for most iterative learning control schemes is relaxed. By combining appropriate Lyapunov-Krasovskii functional and fuzzy logic systems approximation technique, the proposed control scheme can guarantee that the output tracking converges to the desired reference trajectory within an error tolerance and all the closed-loop signals remain bounded.

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