The significance of Bodes Ideal Transfer Function (BITF) in control theory has motivated researchers to explore new ways to harness its beneficial properties. In this work, we present a novel control strategy that integrates Input-Output Feedback Linearization (IOFL) with a Fractional Integral Controller (FIC) to achieve enhanced system performance. The proposed approach is applied to a Two-Input Two-Output (TITO) nonlinear system, where IOFL is first utilized to linearize and decouple the system into two independent pure integrator subsystems. Subsequently, a newly designed fractional-order integral controller, based on BITF, is implemented to ensure precise reference tracking. To validate the effectiveness of this method, we apply it to a TITO coupled tank (TITOCT) process and compare its performance with Nonlinear Continuous-Time Generalized Predictive Control (NCGPC).

This paper presents a compact multi-physical model for lithium-ion batteries used in electric vehicles (EVs). The proposed framework combines an equivalent circuit for electrical behavior, a lumped thermal model, and a semi-empirical ageing model separating calendar and cycling effects. The model is parameterized and validated using experimental data from Kokam NMC-based High Energy (HE) and High Power (HP) cells. Results show high accuracy in voltage, temperature, and degradation prediction, with relative errors below 2%. The model structure offers a good balance between accuracy and computational cost, making it suitable for EV performance evaluation, ageing prediction, and battery management system integration.

Accurate real-time estimation of battery State of Charge (SOC) and State of Health (SOH) is essential for electric vehicle (EV) performance and safety. We propose a hybrid framework combining deep learning with physics insights using LFP cell data. Our core model is a series CNN-TCN-DNN network trained on raw voltage/current signals; replacing measured temperature with cumulative charge ($\int I d t$) as an input improves accuracy. The model is tested under input noise and bias to ensure robustness. For example, it consistently achieves SOC errors below 2% MAPE and more accurate SOH tracking, enabling more reliable range prediction.

Quadcopter drones are increasingly used in a wide range of applications. However, their development remains limited by key challenges such as power supply constraints, short flight durations, and the high weight-to-energy ratio of onboard batteries. Critical factors such as endurance, mass, cost, and fuel consumption are closely linked to the performance of the energy storage system. This study proposes an optimal sizing methodology for quadcopter drone power systems, aiming to maximize flight autonomy across various sizes. A comparative analysis is conducted to evaluate different energy configurations, including battery-only systems, fuel cell-only systems, and hybrid configurations that integrate both technologies. The objective is to highlight the benefits of hybridization inn in terms of fuel efficiency, cost-effectiveness, and weight reduction over a range of mission durations. over various mission durations. The study identifies the most suitable energy combination to enhance autonomy while adhering to structural and weight constraints inherent to drone platforms. Furthermore, the influence of drone size on flight duration is analyzed. Results show that, up to a certain threshold, larger drones can accommodate more energy storage, thus enabling longer flight times.

In this paper, feedback linearization and linear observer with a new analytical fractional-order proportional integral controller (FPIC) design method for controlling a double-integrating system is proposed. This method is based on Bode’s Ideal Transfer Function (BITF) and frequency domain specifications. For the design of the FPIC, the open loop transfer function associated the double integrating system and the FPIC is referenced to Bode’s ideal transfer function to have a constant phase margin. This property ensures the stability of the closed-loop system and robustness against gain variations. The proposed FPIC approach is successfully applied to the two-tank process. The effectiveness and tracking performance of the control law is demonstrated through simulation and experimental tests.

In the last years have seen an increasing usage of Electrical Vehicle (EV). To guarantee safe and reliable operation, it's necessary to possess the capability to monitor, in real time, the state of health (SOH) of the battery. This paper presents a deep learning method which utilizes a Deep Neural network (DNN) for cell-level capacity estimation based on the voltage, current, and State Of Charge. First, a multi-physical models of the battery is done to extract input and output data for the different learning and testing phases. Second, two machine learning algorithms, including DNN and Convolution Neural Network (CNN), are used to predict SOH. Mean Absolute Error (MAE) and Mean Square Error (MSE) are selected as the evaluation index. The results show that the proposed algorithm DNN has the weakest error, which makes it possible to accurately predict the SOH and to have a better stability.

This paper presents an adaptive model-free control strategy designed for trajectory tracking of a 2-DOF Quanser Aero 2 helicopter. The approach employs a fuzzy logic system to approximate a model-based control law, effectively addressing system uncertainties and specific disturbances. The stability analysis of the closed-loop system is carried out using Lyapunov's theorem, guiding the adaptive law to adjust the parameters of the fuzzy system. It is proven that all signals within the closed-loop system remain bounded, and the tracking error converges to a small region near the origin. The proposed adaptive approach is successfully applied to control pitch and yaw angles. Through simulation and experimental tests, the effectiveness and tracking performance of the control law are demonstrated.

Currently, a major obstacle faced by multirotor unmanned aerial vehicles (UAVs) is the limitation of embedded energy. The research community has been exploring this issue through the analysis of design, control, and planning methods. Most of these strategies rely on comprehending the interrelated sub-dynamics of the DAV system, encompassing factors like propeller aerodynamics, electro-mechanical dynamics of the motor, and battery dynamic. This paper proposes an estimation algorithm based on a power consumption multiphysical model, which involves all these components and their coupling. Initially, we build sub-models for each component dynamics and then incorporate them to draw up a multiphysical model that can predict with some degree of precision the consumed power during the UAV flight operation.

The interest to electrify all modes of transportation has increased over these past years. Their source of power is primarily a systems of energy storage like Battery packs. The SOC of a battery indicates the amount of charge stored in the battery and is a critical parameter for its safe and efficient operation of the electric vehicles. However, it's not possible to be measured directly, but can only be estimated from related measurable variables. Data-driven methods have gained significant attention in recent years due to their ability to estimate SOC accurately, even under dynamic operating conditions. This paper provides a data-driven method for SOC estimation based on the Temporal Convolutional neural network (TCN) approach.

This paper focuses on the optimization of the sizing of the on-board storage system in an electric vehicle. The source studied is composed of a combination of two Li-ion battery technologies (a high-power density battery and another high energy density). The interest of this hybridization will be illustrated by comparing the performance of this solution with that of a conventional source (high power battery). A study on the various possible hybridizations is detailed. Following this study, a report is drawn up on the interest of each hybridization according to constraints related to the type of mission, desired autonomy, weight, volume and lifespan of the storage system. The conclusion of this work led us to the need for a very precise choice of the auxiliary storage system. The latter must ensure sufficient autonomy to respect the constraints linked to the aging of the source.

The study of control techniques for exoskeleton robots used in upper-extremity rehabilitation is gaining popularity. These robots are connected to the human upper limb at multiple points, allowing for smooth and independent joint movements. Therefore, providing a robust, precise, and safe control system is necessary. Sliding control-based approaches are well reputed for their robustness to parameter uncertainties, modeling errors, and external disturbances. Nevertheless, their major disadvantage is chattering. This paper describes two controllers based on sliding mode theory to reduce this undesirable effect: the generalized variable structure control and the higher-order finite-time sliding mode control. The former incorporates the derivative of the torque input in the model, while the latter is based on homogeneity and higher-order sliding modes to diminish chattering. A comparison between the two controllers is achieved by simulating passive rehabilitation mode.

The interest of research and automotive industries is concentrating progressively on the Electric Vehicles (EV) which are a global transportation development currently in order to achieve considerable carbon emission reductions. Electric batteries are the essential component of the EV and precise remaining useful life prediction is the main to ensure its reliability. As a result, the inside workings of these battery systems must be fully included. There is presently no precise model for predicting an EV battery's aging. This paper presents an intelligent method for estimating the State Of Charge (SOC) of the battery.

Photovoltaic systems are one of methods of using solar energy, which is one of the most renewable sources. The growth of photovoltaic systems has led to many technical problems, including control; low efficiency and stable provided power. Therefore, we propose in this paper a detailed study of a hybrid system consisting of a PV generator as a primary source and batteries as auxiliary one. Maximum Power Point Tracking (MPPT) algorithm is used to control the boost converter in the objective to increase the PV efficiency, between load and PV generator. This structure can be used as a power supply for standalone system or a solar electric vehicle.The main contribution in this paper is to propose a hybrid FLC/PI supervisor for DC bus voltage control. It should satisfy the load power requirement via the DC-DC bidirectional converter control with optimizing energy transfer. The obtained results using this controller are compared with PI and FLC controllers in term of robustness, stability and dynamic performances. It is confirmed that the proposed control can improve greatly the dynamic performances and robustness of the hybrid PV system.

The embedded storage system is the principal part in the electric vehicle. The vehicle's autonomy and price influence depend on the chosen source technology. For that, we must optimize the sizing and the ageing of the storage system using heavy-duty models. These last ones must take into account the battery behavior and the system cost. The development of an accurate multi-physical lithium battery model can be an extremely time-consuming process because of the complexity of the battery electrochemical phenomena that could occur during an automotive application. In this paper, we will start by introducing the proposed dynamic battery model, and submit the possibility of building an accurate dynamic design while highlighting the key objective of our study, which is the optimization algorithms performances comparison in terms of precision and computing time.

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study, we introduce an optimization algorithm to minimize the consumed energy in the flying vehicle mission under windy conditions. In order to calculate the energy consumed by the quadrotor, we present a power loss model, where the energy is formulated as a function of rotor speed and acceleration. Then, we formulate the energy minimization problem as an optimal control problem and solve this problem in order to calculate minimum energy for a point-to-point quadrotor mission under windy conditions. In order to highlight the proposed optimization approach, we compare energy consumption obtained by optimization algorithm with an adaptive control approach in simulation experiment.

The aim of this paper is to develop multi-physical model to validate the performance on power management strategies developed in our laboratory. The different parts of this model (electrical, thermal and ageing models) will be described and validated using experimental results. In our case, the hybrid energy storage system is composed with two Li-ion cells, High energy modules and high power modules. The obtained results prove the good modelling of the two technologies with just 2% of error between the experimental data and the model data.

A comparative study of two Hybrid Energy Storage Systems (HESS) for automotive applications in terms of weight, volume and cost is undertaken. Main source is a High Energy (HE) density battery while secondary source can be an Ultra High Power (UHP) battery or a Super capacitor (SC). Simulation results show that gains in weight and volume are obtained when HESS uses an UHP secondary source instead of a SC. Drive range is found to have no effect on HESS sizing. Sizing algorithm is used to find an “optimal” solution that improves electric vehicles performance.

In this paper, a disturbance observer based robust adaptive fuzzy tracking control algorithm is developed. The problem of trajectory tracking and wind disturbance rejection for a quadrotor unmanned aerial vehicle are investigated. An adaptive fuzzy controller is used to provide good tracking performances for the quadrotor vehicle. While, a state and disturbance observer is employed in order to estimate the immeasurable states and the unknown wind disturbances. The stability analysis of the global controller/observer system is proved using Lyapunov theory. It is shown that all signals in the closed-loop system are uniformly ultimately bounded (UUB). The proposed design can guarantees the desired tracking performances and external wind disturbance rejection. Simulation studies are presented to highlight the efficiency of the proposed control scheme.

In this paper we deals with the limitation of embedded energy for quadrotor unmanned aerial vehicles. Quadrotor UAVs are flying machines that use lift generated by several rotors, and because of this, a large proportion of their available energy is consumed by rotors in order to maintain the vehicle in the air. In this concept, two optimal control problems are formulated and solved. For the first problem, minimum-energy control effort is computed for desired initial and final configurations with respect to the angular velocity of rotors. Where in the second, minimum-time control effort is computed for a desired energy. The proposed method is illustrated by simulation experiment for a quadrotor UAV.