Experimental comparison of passivity-based controllers for the level regulation of a conical tank

Travieso-Torres, Juan Carlos; Duarte‐Mermoud, Manuel A.; Beytia, Orlando

doi:10.1109/ica-acca.2016.7778487

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…The acquired regulator shows generally excellent outcomes for the reference following alongside load change [16]. Direct combination is utilized to plan of lead-slack compensator connected in series with a PID regulator for all classes of cycles having no less than one shaft at the beginning in addition to time delay and the regulator's boundaries are chosen by changing tuning boundary λ for various vigor levels by assessing Ms esteem [17]. The goal of the proposed work is to use a process model to calculate the controller setting using model-based controller design [18], but the model's structure has not been explicitly used in the controller design.…”

Section: Introductionmentioning

confidence: 99%

Internal Model Controller for Conical Tank System

2024

jeae

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This manuscript proposes an internal model controller (IMC) for conical tank system with hybrid approach. The hybrid approach is the combination of lotus effect optimization (LEA) and attentive evolutionary generative adversarial network (AEGAN) algorithm; hence it is known as LEA-AEGAN approach. The main purpose of the work is to enhanced drainage of process fluid. Process fluids can be of various mixes of solids, semisolids, slurries, and liquids. The inclination angle of the tank aids drainage eliminates sediments, and so on, but it also adds non-linearity into the system. The IMC is used to nonlinear systems, which is required to regulate the level of a conical tank. The proposed LEA is utilized to find the optimal controller parameters and minimize the error of the system. And the AEGAN is used to predicts the control parameters and ensure the controller tuning. By using the proposed system, it is implemented by MATLAB platform and compared with the existing techniques. The settling time, overshoot, and rise time of the proposed controller is 21.9sec, 11.11%, and 2.2sec, respectively

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Section: Introductionmentioning

confidence: 99%

Internal Model Controller for Conical Tank System

2024

jeae

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“…The books [29,30] describe the basis for MRAS tuned via a positive gain. The work [31] uses a fixed-gain experimentally adjusted via trial and error. At the same time, the particle swarm optimization technique tunes the fixed-gain in [32].…”

Section: Introductionmentioning

confidence: 99%

Normalized-Model Reference System for Parameter Estimation of Induction Motors

et al. 2022

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This manuscript proposes a short tuning march algorithm to estimate induction motors (IM) electrical and mechanical parameters. It has two main novel proposals. First, it starts by presenting a normalized-model reference adaptive system (N-MRAS) that extends a recently proposed normalized model reference adaptive controller for parameter estimation of higher-order nonlinear systems, adding filtering. Second, it proposes persistent exciting (PE) rules for the input amplitude. This N-MRAS normalizes the information vector and identification adaptive law gains for a more straightforward tuning method, avoiding trial and error. Later, two N-MRAS designs consider estimating IM electrical and mechanical parameters. Finally, the proposed algorithm considers starting with a V/f speed control strategy, applying a persistently exciting voltage and frequency, and applying the two designed N-MRAS. Test bench experiments validate the efficacy of the proposed algorithm for a 10 HP IM.

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