Iterative Learning Control for Time-Varying Systems Subject to Variable Pass Lengths: Application to Robot Manipulators

This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.

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Initial state learning method for iterative learning control of uncertain time-varying systems

Iterative learning control - Lecture Notes in Control and Information Sciences ◽

10.1007/bfb0110120 ◽

2007 ◽

pp. 79-94

Keyword(s):

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Time Varying ◽

Learning Method ◽

Initial State ◽

Time Varying Systems ◽

State Learning ◽

Uncertain Time

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Data-Driven Networked Optimal Iterative Learning Control for Discrete Linear Time-Varying Systems with One-Operation Bernoulli-Type Communication Delays

Discrete Dynamics in Nature and Society ◽

10.1155/2017/9846846 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yan Geng ◽

Xiaoe Ruan ◽

Hyo-Sung Ahn

Keyword(s):

Iterative Learning Control ◽

Linear Time ◽

Tracking Error ◽

Learning Control ◽

Iterative Learning ◽

Data Driven ◽

Time Varying ◽

Communication Delays ◽

System Matrix ◽

Time Varying Systems

This paper develops a type of data-driven networked optimal iterative learning control strategy for a class of discrete linear time-varying systems with one-operation Bernoulli-type communication delays. In terms of the stochastic Bernoulli-type one-operation communication delayed inputs and outputs, the previous-iteration synchronous compensations are adopted. By means of deriving gradients of two types of objective functions that express the optimal approximation of the system matrix and the minimal tracking error, the strategy approximates the system matrix and upgrades the control inputs in an interact mode as the iteration evolves. By taking advantage of matrix theory and statistical technique, it is derived that the approximation discrepancy of the system matrix is bounded and the mathematical expectation of the tracking error vanishes as the iteration goes on. Numerical simulations manifest the validity and effectiveness.

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