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).

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.

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.

Abstract In the last decade, there has been an extraordinary acceleration in immersive technologies, including virtual reality (VR) and augmented reality (AR). The VR interfaces have widely explored various 3D interaction‐based approaches. 3D interaction is one of the main features of any VR system. However, the problems while selecting and manipulating distant and occluded 3D virtual objects in VR are still unresolved and suffer from a lack of precision and accuracy. To interact with VR/AR systems, various devices such as VR headsets or gloves are used. These devices are not very reliable yet; in fact they are inconvenient and invasive to a person's comfort. Currently, the natural user interfaces (NUIs) are increasingly introduced in human computer interaction (HCI) systems as gestures recognition, which prove to be very useful in the improvement of the user engagement and presence sensing. More particularly, they provide more user‐friendly and nonintrusive 3D interaction methods and techniques. In this article, the Zoom‐fwd, which is an efficient 3D interaction technique is presented. The proposed technique uses gesture recognition for different 3D interaction tasks like selection and manipulation. This new approach allows an efficient interaction with distant and occluded objects, while providing a precise selection, even when the environment is crowded. The Zoom‐fwd technique is a software solution for a number of hardware and software problems. A user study is conducted to determine whether the proposed technique is more suitable when performing interaction tasks. The results show that the Zoom‐fwd technique provides effective interaction with distant and occluded objects, by improving the user task completion performance. In addition, this indicates that selection precision has been enhanced significantly with the Zoom‐fwd technique.

In this work, efficient human activity recognition (HAR) algorithm based on deep learning architecture is proposed to classify activities into seven different classes. In order to learn spatial and temporal features from only 3D skeleton data captured from a “Microsoft Kinect” camera, the proposed algorithm combines both convolution neural network (CNN) and long short-term memory (LSTM) architectures. This combination allows taking advantage of LSTM in modeling temporal data and of CNN in modeling spatial data. The captured skeleton sequences are used to create a specific dataset of interactive activities; these data are then transformed according to a view invariant and a symmetry criterion. To demonstrate the effectiveness of the developed algorithm, it has been tested on several public datasets and it has achieved and sometimes has overcome state-of-the-art performance. In order to verify the uncertainty of the proposed algorithm, some tools are provided and discussed to ensure its efficiency for continuous human action recognition in real time.

For the last decade, a rising need for emotion recognition has been noticed in several domains, such as virtual reality, human-computer interaction, video games and health monitoring, etc. Effectively, emotion recognition via facial expressions attracts increasing attention. Based on geometrical facial features, this paper proposes a new facial emotion recognition method. We collected a novel dataset of 17 subjects facial performance of six emotional states (anger, fear, happiness, surprise, sadness, and neutral) using Kinect (vi) and Kinect (v2) and RGB HD camera. New positional features including a combination of angle and distance features are used to train the classifier. The K nearest neighbors (k-NN) is used as the main classification technique. To assess our proposed method performance, we use the leave-one-out subject cross-validation. A comparison between RGB and RGB-D data is provided. The obtained results show the superior performance of the RGB-D features provided by Kinect (v2). We observed in our experiment that the 2D images are not robust enough for facial emotion recognition due to the sensitivity of the RGB camera to the surrounding conditions.

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The Hough transform is a powerful tool in image analysis, e.g. circle detection is a fundamental issue in image processing applications of industrial parts or tools. Because of its drawbacks, various modifications of the basic circle Hough transform (CHT) method have been suggested. This paper presents a modified method based on the basic CHT algorithm and using no trigonometric calculations in order to improve the computational performance of the voting process for a good accuracy and robustness of circle detection in a binary image. This paper also provides the errors analysis of the proposed method against the basic CHT method to illustrate that it can replace the basic CHT method for small values of the resolution e of the angle θ. It then compete the CORDIC algorithm when implemented into digital device.

In this paper a set of concepts to design mixed reality systems is presented. The principle of interaction space is proposed and then integrated in a development process, especially, to take into account the mixed reality specifications in the information system design. Models are elaborated to highlight relationships between business and interaction spaces. The development of the proposed method is presented on a case study.

This paper describes the use of the probabilistic motion planning technique SBL "single-query bidirectional probabilistic algorithm with lazy collision checking" or in motion planning for robot manipulators. We present a novel strategy to remedy the PRM "probabilistic roadmap" paths which are both excessively long and discontinuous in velocity and acceleration. The optimization of the path will be done first through coarse optimal lazy A* optimization and then through a fine cutting-triangles-edge one, the edges discontinuities are smoothed with cubic polynomials taking the robot's specific dynamic and cinematic constraints. The whole strategy is applied to the 6 axes robot Manipulator MOTOMAN SV3X.