PD-type iterative learning control for linear continuous systems with arbitrary relative degree

2018 ◽  
Vol 41 (9) ◽  
pp. 2555-2562 ◽  
Author(s):  
Qin Fu ◽  
Lili Du ◽  
Guangzhao Xu ◽  
Jianrong Wu ◽  
Pengfei Yu
Keyword(s):  
Iterative Learning Control ◽  
Contraction Mapping ◽  
Learning Control ◽  
Mapping Method ◽  
Iterative Learning ◽  
Relative Degree ◽  
Time Interval ◽  
Continuous Systems ◽  
Iteration Index ◽  
Learning Schemes

This article investigates the iterative learning control problem for linear continuous systems with fixed initial shifts. The systems have arbitrary relative degree and PD-type learning schemes are proposed. Under the effect of the PD-type learning schemes, the output-limiting trajectory is constructed. Based on the contraction mapping method, we show that the schemes can guarantee that the output of the iterative system converges uniformly to the output-limiting trajectory on the finite-time interval as the iteration index tends to infinity. A simulation example is used to illustrate the effectiveness of the proposed method.

Decentralized iterative learning control for switched large-scale systems

2018 ◽  
Vol 41 (4) ◽  
pp. 1045-1056
Author(s):  
Panpan Gu ◽  
Senping Tian ◽  
Qian Liu
Keyword(s):  
Iterative Learning Control ◽  
Large Scale ◽  
Contraction Mapping ◽  
Learning Control ◽  
Mapping Method ◽  
Iterative Learning ◽  
Control Law ◽  
Time Interval ◽  
Large Scale Systems ◽  
Numerical Examples

This paper is concerned with the iterative learning control problem for switched large-scale systems. According to the characteristics of the systems, a decentralized D-type iterative learning control law is proposed for such switched large-scale systems. The proposed controller of each subsystem relies only on local output variables, without any information exchanges with other subsystems. By using the contraction mapping method, it is shown that the algorithm can guarantee that the output of each subsystem converges to the desired trajectory over the whole time interval along the iteration axis. Finally, three numerical examples are given to illustrate the effectiveness of the proposed algorithm.

scholarly journals A PD-Type Iterative Learning Control for a Class of Switched Discrete-Time Systems with Model Uncertainties and External Noises

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Yang
Keyword(s):  
Discrete Time ◽  
Switched Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Monotone Convergence ◽  
Model Uncertainties ◽  
Time Systems

A PD-type iterative learning control algorithm is applied to a class of linear discrete-time switched systems for tracking desired trajectories. The application is based on assumption that the switched systems repetitively operate over a finite time interval and the switching rules are arbitrarily prespecified. By taking advantage of the super-vector approach, a sufficient condition of the monotone convergence of the algorithm is deduced when both the model uncertainties and the external noises are absent. Then the robust monotone convergence is analyzed when the model uncertainties are present and the robustness against the bounded external noises is discussed. The analysis manifests that the proposed PD-type iterative learning control algorithm is feasible and effective when it is imposed on the linear switched systems specified by the arbitrarily preset switching rules. The attached simulations support the feasibility and the effectiveness.

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.

D-type iterative learning control without resetting condition for robot manipulators

Robotica ◽  
2011 ◽  
Vol 29 (7) ◽  
pp. 975-980 ◽  
Author(s):  
Farah Bouakrif
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Initial State ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Topological Measure

SUMMARYThis paper deals with iterative learning control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A D-type ILC is presented with an initial condition algorithm, which gives the initial state value in each iteration automatically. Thus, the resetting condition (the initial state error is equal to zero) is not required. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of this control scheme, over the whole finite time-interval, when the iteration number tends to infinity. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

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