A convex optimization design of robust iterative learning control for linear systems with iteration‐varying parametric uncertainties

Nguyễn, Đình Hòa; Banjerdpongchai, David

doi:10.1002/asjc.266

Cited by 20 publications

(16 citation statements)

References 14 publications

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“…Therefore, the ILC design with iteration-varying factors has been frequently investigated. For example, the iteration-varying initial state [4,5], reference [6,7], parameters [8][9][10], uncertainties [11], and disturbances [12,13] have been frequently encountered. In practice, along the iterative axis, these factors can be described by high-order internal models (HOIMs) [9,14,15], for example,…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Linear Discrete‐Time Systems with Unknown High‐Order Internal Models: A Time‐Frequency Analysis Approach

Zhu

Huang

et al. 2017

Asian Journal of Control

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This work focuses on the iterative learning control (ILC) for linear discrete-time systems with unknown initial state and disturbances. First, multiple high-order internal models (HOIMs) are introduced for the reference, initial state, and disturbances. Both the initial state and disturbance consist of two components, one strictly satisfies HOIM and the other is random bounded. Then, an ILC scheme is constructed according to an augmented HOIM that is the aggregation of all HOIMs. For all known HOIMs, an ILC design criterion is introduced to achieve satisfactory tracking performance based on the 2-D H ∞ theory. Next, the case with unknown HOIMs is discussed, where a time-frequency-analysis (TFA)-based ILC algorithm is proposed. In this situation, it is shown that the tracking error inherits the unknown augmented HOIM that is an aggregation of all unknown HOIMs. Then, a TFA-based method, e.g., the short-time Fourier transformation (STFT), is employed to identify the unknown augmented HOIM, where the STFT could ignore the effect of the random bounded initial state and disturbances. A new ILC law is designed for the identified unknown augmented HOIM, which has the ability to reject the unknown the initial state and disturbances that strictly satisfy HOIMs. Finally, a gantry robot system with iteration-invariant or slowly-varying frequencies is given to illustrate the efficiency of the proposed TFA-based ILC algorithm.

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Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Linear Discrete‐Time Systems with Unknown High‐Order Internal Models: A Time‐Frequency Analysis Approach

Zhu

Huang

et al. 2017

Asian Journal of Control

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“…The key feature of this method is to use the output error obtained from last and/or current iteration to enable the output converges to a desired trajectory. Robustness has been studied in ILC from a number of different perspectives, such as stochastic noise , initial input error , model uncertainty , disturbance rejection and parameter optimization .…”

Section: Introductionmentioning

confidence: 99%

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise

Huang¹,

Fang²,

Wang³

2015

Asian Journal of Control

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Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result.

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“…However, in practical situations, due to disturbances in the working environment, for example, the system matrix may not be the same throughout the iteration domain. In [10], the authors discuss a class of linear systems with iteration-varying parameters, where the control input signals are constrained, but in their work they assume the lifted system, which is a representation that focuses on the system behavior over the iteration domain, is parameterizable. In addition, they assume the lifted system is affine with respect to these parameters, which makes the discussion restricted to a certain class of problems.…”

Section: Introductionmentioning

confidence: 99%

Convex optimization based iterative learning control for iteration-varying systems under output constraints

Jin

Wang

Kwong

2014

11th IEEE International Conference on Control &Amp; Automation (ICCA)

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In this work, we discuss a class of linear iterative learning control (ILC) systems which are iteration-varying with system output constraints. It can be shown that the objective of ensuring convergence of system output tracking error and satisfying system output constraints can be converted to a convex optimization problem, in which the objective function is quadratic and the constraints are convex. Under the proposed algorithm, tracking error convergence can be guaranteed over the iteration domain. A simulation study based on a wafer stage system is presented to demonstrate the efficacy of our approach.

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A convex optimization design of robust iterative learning control for linear systems with iteration‐varying parametric uncertainties

Cited by 20 publications

References 14 publications

Iterative Learning Control for Linear Discrete‐Time Systems with Unknown High‐Order Internal Models: A Time‐Frequency Analysis Approach

Iterative Learning Control for Linear Discrete‐Time Systems with Unknown High‐Order Internal Models: A Time‐Frequency Analysis Approach

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise

Convex optimization based iterative learning control for iteration-varying systems under output constraints

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