We investigate the detection and accommodation of sensor faults for a proton exchange membrane electrolyzer coupled to a DC/DC converter powered by renewable energy sources. The proposed method for detecting and accommodating the sensor fault is model-free and is based on the concept of ultra-local model that is becoming classic in control engineering. The existing literature on active control tolerant to sensor fault dedicated to this question shows that no previous work has addressed this topic. Our approach mitigates the effect of sensor fault on closed-loop behavior and guarantees the stability and performance of the overall system. Numerical simulations under variations in renewable energy sources validate our approach.

Our objective is to study the performance and robustness of the model-free strategy for controlling the oxygen stoichiometry of a fuel cell air supply system with a proton exchange membrane. After reviewing the literature on modeling and control of this process, the model-free approach appears to be a good candidate because, on the one hand, it allows straightforward real-time adaptation to track operating points and, on the other hand, it requires a low computational burden, which is attractive for industrial applications. Numerical simulations for two scenarios (constant and variable oxygen stoichiometry) with two current profiles reveal satisfactory performance of the model-free control law. The robustness is addressed by considering significant variations in the parameters of the proton exchange membrane air supply system.

This research study validates the recently invented Model-free Control (MFC) approach via algebraic estimation to a variable-speed wind turbine. Instead of counting on adaption rules or neural networks, this novel technique employs an ultra-local model for rapid algebraic parameter estimation, requiring no statistical expertise about system models, disturbances, or interdependence between inputs and outputs. In low-speed winds, a model-free controller is designed based on a wind estimator to track the optimum value of power coefficients. In high-speed winds, MFC is extended to a multi-variable model-free controller (MV MFC) in pitch and torque to regulate the produced power around its nominal value. The approach’s key advantage lies in eliminating complex non-linear models, ensuring stability, and reducing non-linearities, uncertainties, and perturbations. The controllers have been tested with a wind turbine mathematical model and validated with FAST simulator in the presence of measurement noise and disturbances. The results show the proposed controller’s superiority over the existing techniques and improvement of power capture.

The Sterile Insect Technique (SIT) against insect pests and insect vectors consists of releasing males that have been previously sterilized in order to reduce or eliminate a specific wild population. We study this complex control question via model-free control, ultra-local models, and intelligent proportional controllers that have already proven their effectiveness in various fields. They permit addressing, perhaps for the first time, the essential sampling question. Computer simulations are displayed and discussed.

An active model-free sensor fault-tolerant control approach is presented in this paper. The proposed method is based on a model-free controller that has demonstrated an effective ability to work without any analytical model knowledge. The active fault-tolerant control procedure has three stages: firstly, the model-free controller is designed using an ultra-local model; secondly, this ultra-local model is used to detect and estimate the sensor fault; thirdly, the obtained estimation is used to adapt the control law according to the sensor fault. The aim of the proposed active fault-tolerant control procedure is to ensure that the regulated output, but not the measured one, tracks the desired trajectory despite the occurrence of a sensor fault. Additive or multiplicative sensor fault is considered for systems that have a linear relationship between control input and system output for any reached steady state. This kind of system includes linear ones and some specific nonlinear systems. The developed methods are validated via numerical simulations for unstable linear and nonlinear systems, with and without saturation of the control input.

Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson's disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13–30 Hz) activity as a feedback signal, offers the potential to adapt DBS automatically in response to changes in patient symptoms and side effects. However, clinically implemented closed-loop techniques have been limited to date to simple control algorithms, due to the inherent uncertainties in the dynamics involved. Model-free control, which has already seen successful applications in the field of bioengineering, offers a way to avoid this limitation and provides an alternative method to apply modern control approach to selective suppression of pathological oscillations.

A novel model-free sensor fault accommodation approach is proposed in this paper. One of the main problems associated with sensor faults in closed-loop systems is that it is the measurement that follows the desired operating point, but not the output to be controlled. The proposed approach is not model-based and works when the closed loop is in steady-state behaviour. First, the sensor fault is estimated using only the closed-loop static characteristic. Second, this estimation allows to accommodate the control law so that the operating point of the regulated output follows the desired setpoint. The sensor fault accommodation procedure is developed for both linear and nonlinear systems. Unlike in the linear case, the accommodation procedure is iterative in the nonlinear one, and a sufficient convergence condition is given in the latter case. The validation of the sensor fault accommodation strategy is carried out for both linear and nonlinear systems by embedding it in a whole fault detection, identification and accommodation (FDIA) procedure, where the choices of the control law design and of the sensor fault detection are model-free approaches given in the literature.

Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson’s disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13–30 Hz) activity as a feedback signal, offers the potential to adapt DBS automatically in response to changes in patient symptoms and side effects. However, clinically implemented closed-loop techniques have been limited to date to simple control algorithms, due to the inherent uncertainties in the dynamics involved. Model-free control, which has already seen successful applications in the field of bioengineering, offers a way to avoid this limitation and provides an alternative method to apply modern control approach to selective suppression of pathological oscillations. In this paper, we use a computational mean-field model of parkinsonian brain activity to show that model-free control provides selective disruption of pathological beta activity within the network. We show that this technique successfully detects and suppresses the beta activity, while preserving the non-pathological activity in the gamma ( ≥ 30 Hz) frequency band, even in the presence of extraneous noise. These results demonstrate the potential for MFC as a viable candidate for closed-loop DBS algorithm in the treatment of Parkinson’s disease.

This communication on collision avoidance with unexpected obstacles is motivated by some critical appraisals on reinforcement learning (RL) which "requires ridiculously large numbers of trials to learn any new task" (Yann LeCun). We use the classic Dubins' car in order to replace RL with flatness-based control, combined with the HEOL feedback setting, and the latest model-free predictive control approach. The two approaches lead to convincing computer experiments where the results with the model-based one are only slightly better. They exhibit a satisfactory robustness with respect to randomly generated mismatches/disturbances, which become excellent in the model-free case. Those properties would have been perhaps difficult to obtain with today's popular machine learning techniques in AI. Finally, we should emphasize that our two methods require a low computational burden.

Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thanks to a new perspective on optimal control combined with recent advances in the field of model-free control (MFC). It is replacing Dynamic Programming, the Hamilton-Jacobi-Bellman equation, and Pontryagin's Maximum Principle. The computing burden is low. The implementation is straightforward. Two nonlinear examples, a chemical reactor and a two tank system, are illustrating our approach. A comparison with the HEOL setting, where some expertise of the process model is needed, shows only a slight superiority of the later. A recent identification of the two tank system via a complex ANN architecture might indicate that a full modeling and the corresponding machine learning mechanism are not always necessary neither in control, nor, more generally, in AI. Copyright (C) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Artificial neural networks and their applications in deep learning have recently made an incursion into the field of control. Deep learning techniques in control are often related to optimal control, which relies on Pontryagin maximum principle or the Hamilton-Jacobi-Bellman equation. They imply control schemes that are tedious to implement. We show here that the new HEOL setting, resulting from the fusion of the two established approaches, namely differential flatness and model-free control, provides a solution to control problems that is more sober in terms of computational resources. This communication is devoted to the synchronization of the popular Kuramoto's coupled oscillators, which was already considered via artificial neural networks (Böttcher et al., Nature Communications 2022), where, contrarily to this communication, only the single control variable is examined. One establishes the flatness of Kuramoto's coupled oscillator model with multiplicative control and develops the resulting HEOL control. Unlike many exemples, this system reveals singularities that are avoided by a clever generation of phase angle trajectories. The results obtained, verified in simulation, show that it is not only possible to synchronize these oscillators in finite time, and even to follow angular frequency profiles, but also to exhibit robustness concerning model mismatches. To the best of our knowledge this has never been done before. Concluding remarks advocate a viewpoint, which might be traced back to Wiener's cybernetics: control theory belongs to AI.

This work presents a new approach to the guidance and control of marine craft via HEOL, i.e., a new way of combining flatness-based and model-free controllers. Its goal is to develop a general regulator for Unmanned Surface Vehicles (USV). To do so, the well-known USV maneuvering model is simplified into a nominal Hovercraft model which is flat. A flatness-based controller is derived for the simplified USV model and the loop is closed via an intelligent proportional-derivative (iPD) regulator. We thus associate the well-documented natural robustness of flatness-based control and adaptivity of iPDs. The controller is applied in simulation to two surface vessels, one meeting the simplifying hypotheses, the other one being a generic USV of the literature. It is shown to stabilize both systems even in the presence of unmodeled environmental disturbances.

Linear time-invariant control systems can be considered as finitely generated modules over the commutative principal ideal ring ℝ[d/dt] of linear differential operators with respect to the time derivative. The Kalman controllability in this algebraic language is translated as the freeness of the system module. Linear quadratic regulators rely on quadratic Lagrangians, or cost functions. Any flat output, i.e., any basis of the corresponding free module leads to an open-loop control strategy via an Euler-Lagrange equation, which becomes here a linear ordinary differential equation with constant coefficients. In this approach, the two-point boundary value problem, including the control variables, becomes tractable. It yields notions of optimal time horizon, optimal parameter design and optimal rest-to-rest trajectories. The loop is closed via an intelligent controller derived from model-free control, which is known to exhibit excellent performance concerning model mismatches and disturbances.

Recent advances in control theory yield closed-loop neurostimulations for suppressing epileptiform seizures. These advances are illustrated by computer experiments which are easy to implement and to tune. The feedback synthesis is provided by an intelligent proportional-derivative (iPD) regulator associated to model-free control. This approach has already been successfully exploited in many concrete situations in engineering, since no precise computational modeling is needed. iPDs permit tracking a large variety of signals including high-amplitude epileptic activity. Those unpredictable pathological brain oscillations should be detected in order to avoid continuous stimulation, which might induce detrimental side effects. This is achieved by introducing a data mining method based on the maxima of the recorded signals. The real-time derivative estimation in a particularly noisy epileptiform environment is made possible due to a newly developed algebraic differentiator. The virtual patient is the Wendling model, i.e., a set of ordinary differential equations adapted from the Jansen-Rit neural mass model in order to generate epileptiform activity via appropriate values of excitation- and inhibition-related parameters. Several simulations, which lead to a large variety of possible scenarios, are discussed. They show the robustness of our control synthesis with respect to different virtual patients and external disturbances.

An active model-free sensor fault tolerant control approach is presented in this paper. The proposed method is based on a model-free controller that has demonstrated an effective ability to work without any analytical model knowledge. The active fault tolerant control procedure has three stages: firstly, the model-free controller is designed using an ultra-local model; secondly, this ultra-local model is used to detect and estimate the sensor fault; thirdly, the obtained estimation is used to adapt the control law according to the sensor fault. The aim of the proposed active fault tolerant control procedure is to ensure that the regulated output, but not the measured one, tracks the desired trajectory despite the occurrence of a sensor fault. The developed method is validated via numerical simulations for both stable and unstable linear systems. The performances of the developed active fault tolerant control procedure for unstable systems are evaluated with and without saturation of the control input.

We present the algebraic foundations of the HEOL setting, which combines flatness-based control and intelligent controllers, two advances in automatic control that have been proven in practice, including in industry. The result provides a solution to many pending questions on feedback loops concerning flatness-based control and model-free control (MFC). Elementary module theory, ordinary differential fields and the generalization of Kähler differentials to differential fields provide an intrinsic definition of the tangent linear system. The algebraic manipulations associated with the operational calculus lead to homeostat and intelligent controllers. They are illustrated via some computer simulations.

Algebraically speaking, linear time-invariant (LTI) systems can be considered as modules. In this framework, controllability is translated as the freeness of the system module. Optimal control mainly relies on quadratic Lagrangians and the consideration of any basis of the system module leads to an open-loop control strategy via a linear Euler-Lagrange equation. In this approach, the endpoint is easily assignable and time horizon can be chosen to minimize the criterion. The loop is closed via an intelligent controller derived from model-free control, which exhibits excellent performances concerning model mismatches and disturbances. The extension to nonlinear systems is briefly discussed.

This communication is devoted to a com-parison between various meteorological forecasts, for the pur-pose of energy management, via different time series tech-niques. The first group of methods necessitates a large num-ber of historical data. The second one does not and is mucheasier to implement, although its performances are todayonly slightly inferior. Theoretical justifications are related tomethods stemming from a new approach to time series, artifi-cial neural networks, computational intelligence and machinelearning. Several numerical simulations are provided and dis-cussed.

This paper presents a new diagnosis view of fuel cell water management. The faults related to water management in the fuel cell are addressed from the control point of view and their occurrence is considered as a consequence of a temperature sensor fault. To ensure proper operation of the polymer electrolyte membrane fuel cell (PEMFC), the stack temperature and inlet pressure are controlled by a model-free control. A fault in the temperature sensor that causes an imbalance in water content in the stack is detected by a new fault detection approach in model-free context. The validation of the proposed strategy is performed on a 1.2 kW fuel cell with real time detection of temperature sensor faults, leading to water condensation in the cells or membrane dehydration. Fuel cell control and diagnosis are achieved without any requirement of an accurate system analytical model and additional sensors. However, faults can be detected in quasi static operation only and not un transient state.

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.