Convergence analysis of iterative learning control using pseudospectra

Shahriari, Zahra; Bernhardsson, Bo; Troeng, Olof

doi:10.1080/00207179.2020.1790662

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…for engineering implementation. Therefore, iterative learning control has achieved fruitful research results after more than 30 years of development [25][26][27][28].…”

Section: The Model Of Memristorsmentioning

confidence: 99%

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Resistance Tracking Control of Memristors Based on Iterative Learning

Cao

Qiao

2023

Entropy

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A memristor is a kind of nonlinear two-port circuit element with memory characteristics, whose resistance value is subject to being controlled by the voltage or current on both its ends, and thus it has broad application prospects. At present, most of the memristor application research is based on the change of resistance and memory characteristics, which involves how to make the memristor change according to the desired trajectory. Aiming at this problem, a resistance tracking control method of memristors is proposed based on iterative learning controls. This method is based on the general mathematical model of the voltage-controlled memristor, and uses the derivative of the error between the actual resistance and the desired resistance to continuously modify the control voltage, making the current control voltage gradually approach the desired control voltage. Furthermore, the convergence of the proposed algorithm is proved theoretically, and the convergence conditions of the algorithm are given. Theoretical analysis and simulation results show that the proposed algorithm can make the resistance of the memristor completely track the desired resistance in a finite time interval with the increase of iterations. This method can realize the design of the controller when the mathematical model of the memristor is unknown, and the structure of the controller is simple. The proposed method can lay a theoretical foundation for the application research on memristors in the future.

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“…for engineering implementation. Therefore, iterative learning control has achieved fruitful research results after more than 30 years of development [25][26][27][28].…”

Section: The Model Of Memristorsmentioning

confidence: 99%

“…At the same time, the design of the controller does not depend on the precise model information of the controlled system, and its simple structure is convenient for engineering implementation. Therefore, iterative learning control has achieved fruitful research results after more than 30 years of development [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Resistance Tracking Control of Memristors Based on Iterative Learning

Cao

Qiao

2023

Entropy

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“…Iterative learning control (ILC) has been recognized as one of the most effective intelligent control strategies because it needs less prior knowledge of the system parameter requirements and because of its significant performance (e.g., [1][2][3][4]). The core mission of the ILC mechanism is to design an adequate control input law for achieving perfect repetitive tracking throughout the whole operation duration as the iterations increase.…”

Section: Introductionmentioning

confidence: 99%

The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

2022

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For multi-phase batch processes with different dimensions whose dynamics can be described as a linear discrete-time-invariant system in each phase, a data-driven optimal ILC was explored using multi-operation input and output data that subordinate a tracking performance criterion. An iterative learning identification was constructed to estimate the system Markov parameters by minimizing the evaluation criterion that consists of the residual of the real outputs from the predicted outputs and two adjacent identifications. Meanwhile, the estimated Markov parameters matrix was embedded into the learning control process in the form of an interaction. By virtue of inner product theory, the monotonic descent of the estimation error was derived, which does not restrict the weighting factor at all. Furthermore, algebraic derivation demonstrates that the tracking is strictly monotonically convergent if the estimation error falls within an appropriate domain. Numerical simulations were carried out to illustrate the validity and the effectiveness of the proposed method.

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