Model-based control for tracking and rejection of periodic signals

Kurniawan, Edi; Wardoyo, Riyo; Syafarina, Inna

doi:10.1109/iciteed.2015.7409008

Cited by 1 publication

(3 citation statements)

References 14 publications

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“…The MIT fundamental approach consists of adjusting the parameters of the closed-loop system such that the loss function described in (25) is minimized. To minimize the function ( ), a realistic approach would be to change the parameters of the system in the direction of the negative gradient of :…”

Section: Design Of a Model-reference Adaptive Pid-controllermentioning

confidence: 99%

“…To convert (28) to the time domain, a differential operator is introduced [23]. In (36) represents the cost function in (25), and = , , .…”

Section: Selection Of the Control Algorithm To Achieve Perfect Model mentioning

confidence: 99%

“…The tracking error of the closed-loop system and the reference model is [24,25]: 311 ε(t) is the error between the transition behaviour of the closed-loop system and the reference model. The criterion for optimization in (25) is given by (36).…”

Section: Determination Of the Error Between The Plant States And The mentioning

confidence: 99%

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Design of a model reference adaptive PID control algorithm for a tank system

Mfoumboulou

2021

IJECE

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This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.

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Section: Design Of a Model-reference Adaptive Pid-controllermentioning

confidence: 99%

“…To convert (28) to the time domain, a differential operator is introduced [23]. In (36) represents the cost function in (25), and = , , .…”

Section: Selection Of the Control Algorithm To Achieve Perfect Model mentioning

confidence: 99%