Internal Multimodel Control for Nonlinear Overactuated Systems

Touati, Nahla; Saidi, Imen; Dhahri, Ahmed; Soudani, Dhaou

doi:10.1007/s13369-018-3515-5

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…However, for many physical systems, the inversion task isn't feasible. An approximate inverse is then required [26,27].…”

Section: The Dahlin Deadbeat Imc Controlmentioning

confidence: 99%

“…Linear models can't capture the dynamics of complex systems due to the presence of strong nonlinearities. The effects of these nonlinearities are mostly undesirable and can greatly affect the performance of controllers [26]. To tackle these challenges, multimodal approaches are emerging as promising alternatives to conventional linearization methods.…”

Section: Dahlin Deadbeat Internal Multimodel Control For Nonlinear Mi...mentioning

confidence: 99%

“…Switching technique [26] The errors between model outputs and the actual system responses should then be evaluated.…”

Section: Figmentioning

confidence: 99%

“…The promising outcomes achieved in controlling such systems prompted its broader application to multivariable nonlinear discretetime systems by considering multimodeling strategy [24]. Multimodel methodologies have gained significant traction in both modeling and controlling nonlinear systems [25,26]. This novel approach involves initially developing a model base to describe the MIMO nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

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Dahlin Deadbeat Internal Model Control for Discrete MIMO Systems

Touati,

Saidi

2024

Int. j. electr. comput. eng. syst. (Online)

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Controlling Multiple Input Multiple Output (MIMO) systems present a considerable challenge, particularly when dealing with time delays, nonlinearities, and disturbances. While the Dahlin algorithm and deadbeat control can offer good performance for such systems especially for systems requiring aperiodic responses or those where overshoot and setteling time need to be minimized, their effectiveness can diminish if the model parameters are inaccurate or in the presence of disturbances which lead to steady-state errors. To address these limitations, we propose combining these approaches with Internal Model Control, known for its robustness in handling variations in process dynamics, ensuring accurate setpoint tracking and disturbance rejection. In this paper, we introduce the Dahlin Deadbeat Internal Model Control (DDIMC) for discrete MIMO systems. Initially designed for linear processes with multiple time delays, this control strategy addresses complex control challenges arising from coupling effects and time delays. For nonlinear processes, we extend this controller using a multimodal control strategy which involves describing the nonlinear system with multiple linear discrete models, each paired with a Dahlin Deadbeat controller. A fusion technique is then employed to select the most suitable controller for application. Simulation case studies performed using the MATLAB software validate the effectiveness of these strategies, demonstrating their ability to consistently ensure satisfactory dynamic and robust performance.

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“…However, for many physical systems, the inversion task isn't feasible. An approximate inverse is then required [26,27].…”

Section: The Dahlin Deadbeat Imc Controlmentioning

confidence: 99%

Section: Dahlin Deadbeat Internal Multimodel Control For Nonlinear Mi...mentioning

confidence: 99%

“…Switching technique [26] The errors between model outputs and the actual system responses should then be evaluated.…”

Section: Figmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dahlin Deadbeat Internal Model Control for Discrete MIMO Systems

Touati,

Saidi

2024

Int. j. electr. comput. eng. syst. (Online)

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“…The synthesis of an IMC controller that is equal to the inverse of the model expression and is fundamental to ensure perfect set-point tracking and this represents the basic problem of the IMC approach. In fact, the realization of the direct model inverse is difficult, and sometimes not possible to realize, for many physical systems [8,9]. This perfect controller cannot be implemented for the following reasons.…”

Section: Problem Formulationmentioning

confidence: 99%

A Novel Discrete Internal Model Control Method for Underactuated System

Saidi¹,

Beaojui²,

Xibilia³

2021

Interdisciplinary Description of Complex Systems

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This article provides a comparative analysis of two common control configurations used to control the side-stream distillation used to separate benzene, toluene and xylene as suggested by Doukas and Lyben. Their under-actuated model has been considered as the model of distillation column and the internal model controller is designed considering a Singular Value Decomposition (SVD) and Virtual Inputs (VI) techniques. An internal controller design based on VI is proposed in this article for this kind of underactuated systems. This design is used to control in parallel the distillation process and its model in real time. The proposed controller design is simple and systematic in relation with the desired closed loop specifications of the internal model control structure. Furthermore, the controller obtained ensure robustness to process variations. The SVD technique can realize the decoupling of under-actuated processes and wipe out unrealizable factors by introducing compensation terms, affecting the dynamic of the system. The aim of this article is to make a comparison between our proposed VI controller and the SVD approach. The results we obtained confirmed the potentials of the proposed controller based on VI considering the set point tracking and its robustness.

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