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Summary: This study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system. [ABSTRACT FROM AUTHOR]

One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller's performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort. [ABSTRACT FROM AUTHOR]

This paper suggests to replace Proportional‐Integral and Proportional‐Integral‐Derivative regulators, which play a key rôle in control engineering, by intelligent Proportional‐Derivative feedback loops, or iPDs, which are derived from model‐free control. This standpoint is enhanced by a laboratory experiment. [ABSTRACT FROM AUTHOR]

A continuously time-varying transmission rate is suggested by many control-theoretic investigations on non-pharmaceutical interventions for mitigating the COVID-19 pandemic. However, such a continuously varying rate is impossible to implement in any human society. Here, we significantly extend a preliminary work (M. Fliess, C. Join, A. d'Onofrio, Feedback control of social distancing for COVID-19 via elementary formulae, MATHMOD, Vienna, 2022), based on the combination of flatness-based and model-free controls with respect to the classic parsimonious SIR model. Indeed, to take into account severe uncertainties and perturbations, we propose a feedback control where the transmission rate, i.e., the control variable, is piecewise constant. More precisely, the transmission rate remains constant during an appreciable time interval, which is not too large. Strict extended lockdowns may therefore be avoided. The poor knowledge of fundamental quantities such as the rate of infection hinders a precise calibration of the transmission rate. Thus, the results of our approach ought therefore not to be regarded as rules of action to follow accurately but as a guideline for a wise behaviour. [ABSTRACT FROM AUTHOR]

Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical take-off and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback controller. The proposed flight controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using model-free control algorithms, the proposed flight control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the model-free control performance to that of the Incremental Nonlinear Dynamic Inversion controller, which has been applied to a variety of aircraft providing effective flight performances. [ABSTRACT FROM AUTHOR]

In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed here. Let us point out that a “good” modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to “intelligent” controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this paper is dedicated to present simulation results with actual data. Actual data, used in MATLAB as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatness-based control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems. [ABSTRACT FROM PUBLISHER]

ALINEA, which was introduced almost 30 years ago, remains certainly the most well-known feedback loop for ramp metering control. A theoretical proof of its efficiency at least when the traffic conditions are rather mild is given here, perhaps for the first time. It relies on tools stemming from the new model-free control and the corresponding ‘intelligent’ proportional controllers. Several computer experiments confirm our theoretical investigations. [ABSTRACT FROM AUTHOR]

‘Model-free control’and the corresponding ‘intelligent’ PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PIDs and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them. [ABSTRACT FROM AUTHOR]

In this article, we use a variant of a recently introduced algebraic state estimation method obtained from a fast output signal time derivatives computation process. The fast time derivatives calculations are entirely based on the consequences of using the "algebraic approach" in linear systems description (basically, module theory and non-commutative algebra). Here, we demonstrate, through computer simulations, the effectiveness of the proposed algebraic approach in the accurate and fast (i.e. nonasymptotic) estimation of the chaotic states in some of the most popular chaotic systems. The proposed state estimation method can then be used in a coding–decoding process of a secret message transmission using the message-modulated chaotic system states and the reliable transmission of the chaotic system observable output. Simulation examples, using Chen's chaotic system and the Rossler system, demonstrate the important features of the proposed fast state estimation method in the accurate extraction of a chaotically encrypted messages. In our simulation results, the proposed approach is shown to be quite robust with respect to (computer generated) transmission noise perturbations. We also propose a way to evade computational singularities associated with the local lack of observability of certain chaotic system outputs and still carry out the encrypting and decoding of secret messages in a reliable manner. [ABSTRACT FROM AUTHOR]

We are generating residuals for linear fault diagnosis and isolation which are robust with respect to a large variety of additive disturbances, working in closed-loop, even with uncertain parameters. Several examples with their computer simulations, including a concrete case-study of a two mass system, are enlightening our viewpoint. Our methods, which are mainly of algebraic flavour (module theory, differential algebra, and operational calculus), are borrowed from recent works on control and identification. [ABSTRACT FROM AUTHOR]

We investigate an extension of predictive control, PID controllers, and Smith predictors to some classes of linear delay systems which are most encountered in practice. The predictive part is provided by extending flatness-based predictive control. Generalized PI regulators lead to a 'PID and Smith predictor'-like scheme which permits us to easily handle (deadtime-dominant) unstable systems. Numerous examples with thorough simulations demonstrate the performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

A general flatness-based framework for linear continuous-time predictive control is presented. The mathematical setting, which is valid for multivariable systems, is provided by the algebraic theory of modules where a controllable system corresponds to a finitely generated free module over a principal ideal ring. Any basis of this free module is a flat output which yields an easy calculation of the predicted trajectory. This formalism permits one to handle non-minimum phase systems, system constraints, and to deal with additive perturbations. Three concrete case studies, namely a dc motor, a flexible system, and a cement mill, are analysed and simulations are given. These examples are written in such a way that any reader who is not familiar with module theory may nevertheless grasp the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper we use results and techniques from the theory of rational power series to show that the complement of a one-letter stochastic language is stochastic, but that the family of stochastic languages is closed neither under union and intersection nor under product and homomorphism. We also give a condition on the poles of a rational one-variable power series r to ensure that the stochastic language defined by r and any cut-point is rational. [ABSTRACT FROM AUTHOR]

Differential algebra permits the generalization of several features of symplectic formalism to mechanics with perfect nonholonomic constraints. [ABSTRACT FROM AUTHOR]