Closed-loop parametric identification of continuous-time linear systems is achieved via new algeb... more Closed-loop parametric identification of continuous-time linear systems is achieved via new algebraic techniques, which are employing non-commutative ring theory, module theory, and operational calculus. The efficiency and robustness with respect to noises are demonstrated by three illustrative examples and their computer simulations. In two of them we compare our results with those given by standard adaptive methods.
In this paper we propose a complete strategy for the longitudinal control of automotive vehicles ... more In this paper we propose a complete strategy for the longitudinal control of automotive vehicles in Stop-and-Go situations. Firstly, a upper level grey-box torque control is proposed to compensate for neglected dynamics at chassis level (due for example to road slopes, aerodynamic forces, rolling resistance forces, etc.). Secondly, to obtain the desired torque, we have considered a model-free approach to elaborate the suitable low level engine or braking torque. Convincing simulation results are presented to validate our method.
A new model-free setting and the corresponding "intelligent" P and PD controllers are employed fo... more A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems. Keywords-Longitudinal and lateral vehicle control, modelfree control, intelligent P controller (i-P controller), algebraic estimation, ADAS (Advanced Driving Assistance Systems).
2008 47th IEEE Conference on Decision and Control, 2008
This paper presents a robust stop-and-go control law, especially well adapted to car following sc... more This paper presents a robust stop-and-go control law, especially well adapted to car following scenarios in urban environments. Since many vehicle/road interaction factors (road slope, rolling resistance, aerodynamic forces) are very poorly known and measurements are quite noisy, a robust strategy is proposed within an algebraic framework. On the one hand, noisy signals will be processed in order to obtain accurate derivatives, and thereafter, variable estimates. On the other hand, a grey-box closed-loop control will be implemented to compensate all kind of unmodeled dynamics or parameter uncertainties.
This paper presents a new approach for estimating vehicle velocities at its gravity center. The p... more This paper presents a new approach for estimating vehicle velocities at its gravity center. The proposed strategy relies on recent algebraic techniques for numerical differentiation and diagnosis. We do not use any tire model in order to obtain an estimation, which is robust with respect to model uncertainties (friction, . . . ). All available measurements in a mass-production car are however exploited.
It is shown that the "intelligent" controllers which are associated to the recently introduced mo... more It is shown that the "intelligent" controllers which are associated to the recently introduced model-free control synthesis may be easily implemented on cheap and small programmable devices. Several successful numerical experiments are presented with a special emphasis on fault tolerant control.
Model-free" control and the related "intelligent" proportional-integral (PI) controllers are succ... more Model-free" control and the related "intelligent" proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow speed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key rôle. Several excellent computer simulations are provided and analyzed.
Model-free" control and the related "intelligent" proportional-integral (PI) controllers are succ... more Model-free" control and the related "intelligent" proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow speed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key rôle. Several excellent computer simulations are provided and analyzed.
Estimation algébrique des paramètres intrinsèques d’un signal sinusöıdal biaise ́ en environn... more Estimation algébrique des paramètres intrinsèques d’un signal sinusöıdal biaise ́ en environnement bruité
In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, o... more In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, or ADRC, are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and in the same time for attenuating their severe shortcomings. After a brief review of MFC and ADRC, several examples show the superiority of MFC, which permits to tackle most easily a much wider class of systems.
2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019
Supply chain management and inventory control provide most exciting examples of control systems w... more Supply chain management and inventory control provide most exciting examples of control systems with delays. Here, Smith predictors, model-free control and new time series forecasting techniques are mixed in order to derive an efficient control synthesis. Perishable inventories are also taken into account. The most intriguing "bullwhip effect" is explained and attenuated, at least in some important situations. Numerous convincing computer simulations are presented and discussed. Keywords-Supply chain management, inventory control, perishable inventories, bullwhip effect, delay systems, Smith predictors, model-free control, short-term forecasting, time series, high gain.
International Journal of Robust and Nonlinear Control, 2021
This paper suggests to replace PIs and PIDs, which play a key rôle in control engineering, by int... more This paper suggests to replace PIs and PIDs, 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. Index Terms Model-free control, intelligent proportional-derivative feedback (iPD), PI, PID, Riachy's trick.
Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1, 2020
This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinf... more This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory experiment, which was already investigated via today's ML techniques, is reported in order to confirm this viewpoint.
Aujourd'hui, « commande sans modèle », ou « MFC », et « commande par rejet actif de perturbations... more Aujourd'hui, « commande sans modèle », ou « MFC », et « commande par rejet actif de perturbations », ou « ADRC », sont les approches les plus en vue pour préserver les avantages des PID, si populaires dans l'industrie, tout en atténuant leurs carences. Après un bref rappel sur MFC et ADRC, plusieurs exemples démontrent la supériorité du sans-modèle car permettant d'embrasser une classe beaucoup plus vaste de systèmes. ABSTRACT. In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, or ADRC, are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and in the same time for attenuating their severe shortcomings. After a brief review of MFC and ADRC, several examples show the superiority of MFC, which permits to tackle most easily a much wider class of systems. MOTS-CLÉS. PID, commande sans modèle, commande par rejet actif des perturbations, commande par platitude KEYWORDS.
Recent investigations on the longitudinal and lateral control of wheeled autonomous vehicles are ... more Recent investigations on the longitudinal and lateral control of wheeled autonomous vehicles are reported. Flatness-based techniques are first introduced via a simplified model. It depends on some physical parameters, like cornering stiffness coefficients of the tires, friction coefficient of the road,. .. , which are notoriously difficult to identify. Then a model-free control strategy, which exploits the flat outputs, is proposed. Those outputs also depend on physical parameters which are poorly known, i.e., the vehicle mass and inertia and the position of the center of gravity. A totally model-free control law is therefore adopted. It employs natural output variables, namely the longitudinal velocity and the lateral deviation of the vehicle. This last method, which is easily understandable and implementable, ensures a robust trajectory tracking problem in both longitudinal and lateral dynamics. Several convincing computer simulations are displayed.
"Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a ... more "Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanation via "model-free control" and the corresponding "intelligent" controllers [Fliess M., Join C. (2013). Int. J. Contr., 86, 2228-2252], which are already successfully applied in many concrete situations. As a byproduct this setting provides also a slightly different presentation of homeostasis, or "exact adaptation," where the working conditions are assumed to be "mild." Several convincing, but academic, computer simulations are provided and discussed.
IEEE Transactions on Intelligent Transportation Systems, 2017
In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is ... more In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. 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 work 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 flatnessbased 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.
Closed-loop parametric identification of continuous-time linear systems is achieved via new algeb... more Closed-loop parametric identification of continuous-time linear systems is achieved via new algebraic techniques, which are employing non-commutative ring theory, module theory, and operational calculus. The efficiency and robustness with respect to noises are demonstrated by three illustrative examples and their computer simulations. In two of them we compare our results with those given by standard adaptive methods.
In this paper we propose a complete strategy for the longitudinal control of automotive vehicles ... more In this paper we propose a complete strategy for the longitudinal control of automotive vehicles in Stop-and-Go situations. Firstly, a upper level grey-box torque control is proposed to compensate for neglected dynamics at chassis level (due for example to road slopes, aerodynamic forces, rolling resistance forces, etc.). Secondly, to obtain the desired torque, we have considered a model-free approach to elaborate the suitable low level engine or braking torque. Convincing simulation results are presented to validate our method.
A new model-free setting and the corresponding "intelligent" P and PD controllers are employed fo... more A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems. Keywords-Longitudinal and lateral vehicle control, modelfree control, intelligent P controller (i-P controller), algebraic estimation, ADAS (Advanced Driving Assistance Systems).
2008 47th IEEE Conference on Decision and Control, 2008
This paper presents a robust stop-and-go control law, especially well adapted to car following sc... more This paper presents a robust stop-and-go control law, especially well adapted to car following scenarios in urban environments. Since many vehicle/road interaction factors (road slope, rolling resistance, aerodynamic forces) are very poorly known and measurements are quite noisy, a robust strategy is proposed within an algebraic framework. On the one hand, noisy signals will be processed in order to obtain accurate derivatives, and thereafter, variable estimates. On the other hand, a grey-box closed-loop control will be implemented to compensate all kind of unmodeled dynamics or parameter uncertainties.
This paper presents a new approach for estimating vehicle velocities at its gravity center. The p... more This paper presents a new approach for estimating vehicle velocities at its gravity center. The proposed strategy relies on recent algebraic techniques for numerical differentiation and diagnosis. We do not use any tire model in order to obtain an estimation, which is robust with respect to model uncertainties (friction, . . . ). All available measurements in a mass-production car are however exploited.
It is shown that the "intelligent" controllers which are associated to the recently introduced mo... more It is shown that the "intelligent" controllers which are associated to the recently introduced model-free control synthesis may be easily implemented on cheap and small programmable devices. Several successful numerical experiments are presented with a special emphasis on fault tolerant control.
Model-free" control and the related "intelligent" proportional-integral (PI) controllers are succ... more Model-free" control and the related "intelligent" proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow speed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key rôle. Several excellent computer simulations are provided and analyzed.
Model-free" control and the related "intelligent" proportional-integral (PI) controllers are succ... more Model-free" control and the related "intelligent" proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow speed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key rôle. Several excellent computer simulations are provided and analyzed.
Estimation algébrique des paramètres intrinsèques d’un signal sinusöıdal biaise ́ en environn... more Estimation algébrique des paramètres intrinsèques d’un signal sinusöıdal biaise ́ en environnement bruité
In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, o... more In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, or ADRC, are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and in the same time for attenuating their severe shortcomings. After a brief review of MFC and ADRC, several examples show the superiority of MFC, which permits to tackle most easily a much wider class of systems.
2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019
Supply chain management and inventory control provide most exciting examples of control systems w... more Supply chain management and inventory control provide most exciting examples of control systems with delays. Here, Smith predictors, model-free control and new time series forecasting techniques are mixed in order to derive an efficient control synthesis. Perishable inventories are also taken into account. The most intriguing "bullwhip effect" is explained and attenuated, at least in some important situations. Numerous convincing computer simulations are presented and discussed. Keywords-Supply chain management, inventory control, perishable inventories, bullwhip effect, delay systems, Smith predictors, model-free control, short-term forecasting, time series, high gain.
International Journal of Robust and Nonlinear Control, 2021
This paper suggests to replace PIs and PIDs, which play a key rôle in control engineering, by int... more This paper suggests to replace PIs and PIDs, 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. Index Terms Model-free control, intelligent proportional-derivative feedback (iPD), PI, PID, Riachy's trick.
Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1, 2020
This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinf... more This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory experiment, which was already investigated via today's ML techniques, is reported in order to confirm this viewpoint.
Aujourd'hui, « commande sans modèle », ou « MFC », et « commande par rejet actif de perturbations... more Aujourd'hui, « commande sans modèle », ou « MFC », et « commande par rejet actif de perturbations », ou « ADRC », sont les approches les plus en vue pour préserver les avantages des PID, si populaires dans l'industrie, tout en atténuant leurs carences. Après un bref rappel sur MFC et ADRC, plusieurs exemples démontrent la supériorité du sans-modèle car permettant d'embrasser une classe beaucoup plus vaste de systèmes. ABSTRACT. In today's literature Model-Free Control, or MFC, and Active Disturbance Rejection Control, or ADRC, are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and in the same time for attenuating their severe shortcomings. After a brief review of MFC and ADRC, several examples show the superiority of MFC, which permits to tackle most easily a much wider class of systems. MOTS-CLÉS. PID, commande sans modèle, commande par rejet actif des perturbations, commande par platitude KEYWORDS.
Recent investigations on the longitudinal and lateral control of wheeled autonomous vehicles are ... more Recent investigations on the longitudinal and lateral control of wheeled autonomous vehicles are reported. Flatness-based techniques are first introduced via a simplified model. It depends on some physical parameters, like cornering stiffness coefficients of the tires, friction coefficient of the road,. .. , which are notoriously difficult to identify. Then a model-free control strategy, which exploits the flat outputs, is proposed. Those outputs also depend on physical parameters which are poorly known, i.e., the vehicle mass and inertia and the position of the center of gravity. A totally model-free control law is therefore adopted. It employs natural output variables, namely the longitudinal velocity and the lateral deviation of the vehicle. This last method, which is easily understandable and implementable, ensures a robust trajectory tracking problem in both longitudinal and lateral dynamics. Several convincing computer simulations are displayed.
"Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a ... more "Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanation via "model-free control" and the corresponding "intelligent" controllers [Fliess M., Join C. (2013). Int. J. Contr., 86, 2228-2252], which are already successfully applied in many concrete situations. As a byproduct this setting provides also a slightly different presentation of homeostasis, or "exact adaptation," where the working conditions are assumed to be "mild." Several convincing, but academic, computer simulations are provided and discussed.
IEEE Transactions on Intelligent Transportation Systems, 2017
In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is ... more In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. 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 work 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 flatnessbased 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.
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Papers by Michel Fliess