A new model-free design for vehicle control and its validation through an advanced simulation platform

Menhour, Lghani; d’Andréa-Novel, Brigitte; Fliess, Michel; Gruyer, Dominique; Mounier, Hugues

doi:10.1109/ecc.2015.7330852

Cited by 22 publications

(20 citation statements)

References 22 publications

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“…Unlike [17], the MFC algorithm proposed in this paper has a discrete-time implementation, and it guarantees the CS stability under some assumptions by fulfilling certain constrains. MFC algorithms have also been successfully applied to many processes [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Roman

Rădac

Precup

2016

IET Control Theory & Applications

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This study proposes data-driven model-free adaptive control (MFAC), model-free control (MFC) and virtual reference feedback tuning (VRFT) techniques applied to the control of a representative non-linear multi-input-multi-output (MIMO) system represented by the twin rotor aerodynamic system (TRAS). These data-driven techniques are implemented for both a single MIMO controller and two separately designed single-input-single-output controllers running in parallel for azimuth and pitch control. The three techniques are implemented as MFAC and MFC algorithms and as linear controllers tuned by VRFT. The performance of the three data-driven MIMO control system structures is compared systematically on the basis of the experimental results in terms of the values of the sum of mean squared control errors measured on TRAS equipment.

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Section: Introductionmentioning

confidence: 99%

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Roman

Rădac

Precup

2016

IET Control Theory & Applications

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“…The weight error is defined as w =w * −ŵ , the error equation can be simplified aṡ x= Ax +B (f ( x , w * )+u e +μ )= = Ax+ B (w T φ +μ ) (16) Also,…”

Section: Intelligent Proportion Differential Neural Network Controllermentioning

confidence: 99%

“…In addition, its performance depends on the quality of the observer which requires the ability of rapidity and reliability. Moreover, these methods cannot guarantee the stability in closed-looped system [12,16].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, as a new type of adaptive control, model-free control and its corresponding intelligent proportion integration differentiation (iPID) controller which has been applied successfully to the real-time systems, such as DC-DC converters [17], vehicle control [16], agricultural greenhouses [13], High pressure common rail injection system [22] etc., was firstly introduced in the references [5]. It requires only the systems input gain, and the output measurements.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System

Wang¹,

Li²,

Yang³

et al. 2016

SIC

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This paper presents an intelligent proportion-differential neural network (iPDNN) controller for unknown nonlinear systems. This controller is based on the intelligent proportion integration differentiation (iPID) controller. In an iPID controller system, a unknown nonlinear SISO system is regarded as an ultra-local two-order or one-order model and a lumped unknown dynamics (LUD) disturbance which contains the high-term and parametric uncertainties by the differential algebra and estimation method online. However, its performance of an iPID control depends on the precision and rapidity for estimating the LUD disturbance. Besides, it also influences the parameter in the ultra-local model. In order to compensate the estimation error of LUD disturbance, we put forward an extra radial basis function (RBF) neural network observer to estimate it. This extra observer cannot only ensure to acquire the estimation error rapidly, but also has an ability of self-learning. In addition, this iPDNN method can ensure the closed-loop system stability under the Lyapunov stability theory. Finally, in order to demonstrate its performance, an inverted pendulum plant has been applied and the results indicate this method is of efficiency.

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“…Our purpose is to show that the new model-free control (MFC) [16], which has already been successfully applied in many concrete and diverse situations (see [16,29,38], and the references therein), might get closer to a real artificial pancreas. As a matter of fact the benefits of this control algorithm are numerous:…”

Section: Introductionmentioning

confidence: 99%

A Variable Reference Trajectory for Model-Free Glycemia Regulation

Ridha¹,

Moog²,

Delaleau³

et al. 2015

2015 Proceedings of the Conference on Control and Its Applications

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The control design of an artificial pancreas, a hot research topic in diabetology, is tackled via the newly introduced model-free control and its corresponding "intelligent" proportional controller, which were already quite successful in many concrete and diverse situations. It results in an insulin injection for type 1 diabetes which displays via constant references a good nocturnal/fasting response, but unfortunately a poor postprandial behavior due to long hyperglycemia. When a variable reference is introduced, that switches between a constant one, when glycemia is more or less normal or moderate, and an exponential decay reference path, when a high glycemia rate indicates a meal intake, the results in silico, which employ real clinical data, become excellent. We obtain a bolus-shaped insulin injection rate during postprandial phases. The hyperglycemic peaks are therefore lowered a lot.

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A new model-free design for vehicle control and its validation through an advanced simulation platform

Cited by 22 publications

References 22 publications

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System

A Variable Reference Trajectory for Model-Free Glycemia Regulation

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