Fuzzy iterative learning control applied in a biological reactor using a reduced number of measures

Márquez-Vera, Marco Antonio; Ramos-Velasco, Luis Enrique; Suárez-Cansino, Joel; Márquez-Vera, C.A.

doi:10.1016/j.amc.2014.08.072

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…With the development of decades, ILC has attracted a lot of interests and numerous different ILC methods have been proposed such as high-order ILC, 2 adaptive ILC, [3][4][5][6] robust ILC, 7,8 optimal ILC. 9 In addition, ILC methods have been applied in various fields, for example, robotic manipulator, 10,11 hard disk drive, 12 permanent magnet synchronous motor (PMSM), 13 biological reactor, 14 high-speed trains, 15 and heat flux boundary control. 16 In recent years, ILC based on networked control systems (NCSs) has drawn a lot of attentions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive iteration learning control with iteration‐varying event‐triggered mechanism for discrete‐time nonlinear systems with random initial states

Chai

2023

Intl J Robust & Nonlinear

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In this work, a novel adaptive event‐trigered iterative learning control (ILC) scheme is designed for discrete‐time nonlinear systems with time‐varying parameters and random initial states. The reference trajectory can be iteration‐invariant or iteration‐varying. The control input is updated when the event‐triggering condition is satisfied, which reduces the action time of controller and consequently the unnecessary waste of communication resource. An adaptive event‐triggering condition parameter is designed to improve the efficiency of the event generator. Moreover, the parameter updating law of adaptive iterative learning controller is designed based on the recursive least squares algorithm. With the proposed adaptive ILC with event‐triggered mechanism, the learning convergence along the iteration axis is guaranteed and communication burden is reduced. Finally, rigorous theoretical analysis under Lyapunov theory and illustrative examples are given to demonstrate the efficacy of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive iteration learning control with iteration‐varying event‐triggered mechanism for discrete‐time nonlinear systems with random initial states

Chai

2023

Intl J Robust & Nonlinear

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“…All these problems can be affronted by using some modeled methods [2] or by reinforcement learning [3]. Another problem is the measurement of concentrations, per example in [4] was not defined the sampling rate to have fewer measurements made by hand. Thus, the measures cannot be done in the exact instant.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Observers Applied in Biodegradation

Márquez-Vera

Rodríguez-Romero

Márquez-Vera

et al. 2021

IJRCS

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There exist processes difficult to control because of the lack of inline sensors, as occurs in biotechnology engineering. Commonly the sensor is expensive, damaged, or even they do not exist. It is important to build an observer to have an approximation of the process output to have a closed-loop control. The biotechnological processes are nonlinear, thus in this work is proposed a fuzzy observer to endure nonlinearities. To improve the results reported in the literature, type-2 fuzzy logic was used to justify the membership functions used. The observer's gains were computed via LMIs to guarantee the observer's stability. To facilitate the fuzzy inference computation, interval type-2 fuzzy sets were implemented. The results obtained with the interval type-2 fuzzy observer were compared with a similar technique that uses a fuzzy sliding mode observer; this new approach gives better results obtaining an error 60% lower than the obtained with the other technique. They were designed three observers that work ensemble via a fuzzy relation. The best approximation was to estimate the intermediate concentration. It is important to know this variable because this sub-product was also toxic. It was concluding that by using the oxygen concentration and the liquid volume inside the reactor, the other concentrations were estimated. Finally, this result helps to design a fuzzy controller by using the estimated state. Using this approach, the estimation errors for the phenol and biomass concentrations were 49.26% and 21.27% lower than by using sliding modes.

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Adaptive Iterative Learning Control of Nonlinearly Parameterized Pure Feedback Nonlinear Systems

Benslimane

Boulkroune

Chekireb

2017

J Control Autom Electr Syst

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Fuzzy iterative learning control applied in a biological reactor using a reduced number of measures

Cited by 8 publications

References 20 publications

Adaptive iteration learning control with iteration‐varying event‐triggered mechanism for discrete‐time nonlinear systems with random initial states

Adaptive iteration learning control with iteration‐varying event‐triggered mechanism for discrete‐time nonlinear systems with random initial states

Interval Type-2 Fuzzy Observers Applied in Biodegradation

Adaptive Iterative Learning Control of Nonlinearly Parameterized Pure Feedback Nonlinear Systems

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