Iterative Learning Control

Ahn, Hyo‐Sung; Chen, YangQuan; Moore, Kevin L.

doi:10.1007/978-1-84628-859-3

Cited by 99 publications

(3 citation statements)

References 205 publications

(380 reference statements)

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“…Data generated by this sensor can then be linked with machine-driven, closed-loop control algorithms such as Proportional-Integral-Derivative (PID) 41 and Iterative Learning Controllers (ILC). 42 These systems will allow for the optimization of both continuous and bolus (or oral) therapy to drive individualized target attainment of pre-defined PK/PD indices associated with maximal bacterial killing and/or suppression of the emergence of AMR. 43 , 44 These may be current gold standard PK/PD targets 45 , 46 or novel indices, such as AUC:EC 50 ratio.…”

Section: Concept Of Closed-loop Control For Individualized Antimicrobmentioning

confidence: 99%

Delivering precision antimicrobial therapy through closed-loop control systems

Rawson

O’Hare

Herrero

et al. 2017

Journal of Antimicrobial Chemotherapy

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Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.

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Section: Concept Of Closed-loop Control For Individualized Antimicrobmentioning

confidence: 99%

Delivering precision antimicrobial therapy through closed-loop control systems

Rawson

O’Hare

Herrero

et al. 2017

Journal of Antimicrobial Chemotherapy

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“…In the norm-optimal approach, performance and robustness requirements are prescribed by means of weighting matrices. Addressing the tradeoff between performance and robustness with respect to system uncertainty typically requires repeated user interaction to refine the weighting matrices or advanced synthesis tools from robust control [11]. In contrast, performance and robustness goals in frequency-domain ILC are often specified by means of loop-shaping-based techniques in the frequency domain [10].…”

mentioning

confidence: 99%

Frequency-Domain ILC Approach for Repeating and Varying Tasks: With Application to Semiconductor Bonding Equipment

Boeren

Bareja

Kok

et al. 2016

IEEE/ASME Trans. Mechatron.

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“…Recently, iterative learning control problems of P-type, D-type, I-type, or their combination schemes have been widely applied to various types of repetitive or batch dynamical systems (see, e.g., [32][33][34][35][36][37][38]). The problem on designing an ILC for uncertain plants with time-delays has not been fully investigated, and only a limited number of the results are available so far (see, e.g., [39][40][41]).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

Gao

Wang

Zhou

2017

Discrete Dynamics in Nature and Society

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We address existence and Ulam-Hyers and Ulam-Hyers-Mittag-Leffler stability of fractional nonlinear multiple time-delays systems with respect to two parameters' weighted norm, which provides a foundation to study iterative learning control problem for this system. Secondly, we design PID-type learning laws to generate sequences of output trajectories to tracking the desired trajectory. Two numerical examples are used to illustrate the theoretical results.

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Iterative Learning Control

Cited by 99 publications

References 205 publications

Delivering precision antimicrobial therapy through closed-loop control systems

Delivering precision antimicrobial therapy through closed-loop control systems

Frequency-Domain ILC Approach for Repeating and Varying Tasks: With Application to Semiconductor Bonding Equipment

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

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