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Structural Equation Models allow assessing the conformity of a theoretical model to some given data. The fundamental hypothesis of the covariance-based approach is that the covariance matrix of the model variables can be generated by the model. This matrix is called the implied covariance matrix. Several methods are used for its computation. However, it depends on many assumptions that may sometimes be unrealistic. In this paper, we assess the assumption of the correlation of the error terms and we proposed a correction to the iterative method for the computation of the implied covariance matrix. Theoretical, as well as numerical illustrations, are given to show the advantage of the corrected method.

Path Analysis is a powerful technique to examine the relationships of cause and effect between several phenomena presented as variables. One of its major steps is the computation of the correlation matrix implied by the model. This matrix is useful to estimate parameters as well as to assess the overall fit of the model. In this paper, the concept of the implied correlation matrix is introduced. Besides, two major methods used for its computation are discussed. Finally, comparisons between these methods in terms of hypotheses and with simulated example using Python are proposed.

It is well known that a proportional output feed-back controller $u(t) = -ky(t)$ can stabilize any strict relative degree one, minimum phase multi-input multi-output linear deterministic system with positive definite high frequency gain matrix, if the proportional gain k is high enough. The guiding research idea of this paper is whether it is possible to extend this result to a class of multi-input, multi-output Markovian jump linear systems. Based on some new generalized structural assumptions, a class of high-gain stochastically stabilizable Markovian jump linear systems is constructed. This means that any system belonging to this class can be stabilized by a mode-dependent output feedback controller $u(t)=-k(r_{t})y(t)$, provided the proportional gain for every mode is sufficiently large.

Structural Equation Modeling is a multivariate powerful technique that allows us to analyze causal relationship between hypothetical constructs, each measured by a number of observable variables. The computation of the covariance matrix implied by the structural equation model is a crucial step in the whole modeling process. El hadri and Hanafi have presented in 2015 a method called Finite Iterative Method for this issue. One of its major limitations is that it deals only with models without latent variables (Path Analysis). A generalization of this method for path models with latent variables is presented in this paper. In addition, theoretical and computational comparison with the well-known Joreskog’s method is also discussed and illustrated.

In this paper a novel antenna for 5G applications was proposed. The antenna has a small structure, its dimensions are 10x10 mm2 and it is printed on a Rogers substrate with a dielectric constant of 2.2 and thickness of 0.5 mm to operate at 28 GHz. The Wave Concept Iterative Process (WCIP) method has been applied to determine the electromagnetic characteristics of this antenna. The theoretical formulation of this method is based on two relationships that bind waves, one in the spatial domain and the other in the spectral domain. The passage of waves between the two domains is ensured by the FMT (Fast Modal Transform) and its inverse FMT−1. The comparison of the WCIP results agrees well with the CST simulation data. The main goal of this paper is the using a defected ground structures (DGS) to increase the bandwidth.

This paper presents an adaptive integral terminal sliding mode control strategy applied to an upper limb exoskeleton. The controller is designed to address the problem of non knowledge of the dynamic parameters of the human limb since they differ from a person to another and the compensation for external disturbances. The proposed control scheme which supposes known only the upper bound of the inertia matrix of the system, guarantees high tracking performance when assisting wearers with shoulder, elbow and wrist joint movements. The reaching of the sliding mode as well as the tracking errors and the uncertainty bounds estimation convergence in finite time is proven. Simulations were performed to evaluate the performance and the efficiency of the proposed controller in tracking trajectories that correspond to passive arm movements.

This paper proposes simple and efficient algorithms for implementation of attitude estimation and control based on data fusion using complementary filters taking into account sensors dynamics. First of all, we propose a passive form of the filter by fusing the measured inertial vectors and the gyro measurements in order to reconstruct real inertial vectors which can be used with any algebraic algorithm (TRIAD, QUEST, etc.) that leads to globally asymptotic attitude estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude stabilization. Then, instead of using direct raw measurements in control law we propose a new solution that leads to accurate estimation of inertial vectors by using complementary filters based on sensors dynamics. The stability analysis of the error dynamics based on Lyapunov method proved that almost all trajectories converge asymptotically to the desired equilibrium point. Simulation results show the effectiveness and the performance of the proposed solutions.

In this paper a new method for attitude estimation of rigid body using line-based vision and a Multiplicative Extended Kaiman Filter (MEKF) is developed. A vision-based line-tracking algorithm that allows to detect and to track points and lines along sequence of images without drift is used. From this algorithm, we can get an implicit measure of the lines direction. The latter are then fused with gyro measurements using an observer designed on SO (3) in order to estimate attitude with gyro bias compensation. The gain matrices of the proposed observer are determined based on continuous-time MEKF. The problem of sign ambiguity related to the implicit measure of direction lines is addressed and a correction factor is used to remove this ambiguity. Simulation results has been presented to show the effectiveness of the proposed approach.

This paper addresses the problem of estimation of attitude of accelerated rigid body vehicles moving in 3D-space using IMU and data related to measurement of velocity by GPS. We propose a new observer for attitude estimation based on proportional-integral (PI) compensator along with discontinuous switching function in the estimation of velocity to compensate the effect of linear acceleration. The convergence analysis of the proposed observer is studied using Barbalat's lemma based on a Lyapunov like positive definite function to guarantee asymptotic convergence. The performance of the observer is illustrated by simulation results which show the convergence behaviour of the proposed observer.

We propose a simple and efficient globally asymptotic attitude estimation solution in which complementary linear filters are combined with TRIAD algorithm. The solution leads to many possible combinations between the form of the filter (direct or passive) and its order. Lyapunov analysis results for the proposed direct and passive second order filters are addressed. Global asymptotic convergence of the filtered signals has been shown. The proposed solution includes the estimation of bias of gyro-rate and the possibility of improving the quality of the estimation by increasing the order of the filter. The performance of the filters and the AHRS solutions are demonstrated via many simulations.

We present in this paper a new algorithm for attitude and gyro-bias estimation of a rigid body in rotation in space using low-cost sensors. The algorithm is based on sliding mode nonlinear observer that provides in the same time estimates of the gyro-bias and the actual attitude of the rigid body. This algorithm was developed in order to address the well-known problem of the weak dynamics of the attitude sensors (inclinometers), which can be modeled by low pass filters, and of the measurement bias of the gyros. In its design the observer uses biased gyro measurement and attitude measurements, provided by the low-cost sensors. The stability of the observer was proven using Lyapunov stability method. The effectiveness of the algorithm has been shown from experimental tests using a rotary platform equipped with several sensors with axes of rotation coincide with orientation of the rigid body.

This paper presents a nonlinear observer algorithm for attitude estimation that improves the quality of measures obtained by using low-cost inertial measurements (IMU). It is based on sliding mode observer that provides both the estimates of the gyro-bias and the actual attitude of the rigid body. The algorithm was developed in order to address the well-known problem of the weak dynamics of the tilt sensors and magnetometers, which can be modeled by low pass filters, and of the measurement bias of the gyros. In its design the observer uses the real measurements given by the low-cost attitude sensors (inclinometers and magnetometers) and the gyros, the filters modeling the sensors and the kinematics equation of the rigid body. The asymptotic convergence of the estimation of the attitude and bias-gyros was proven using Lyapunov stability method. The effectiveness of the algorithm has been shown from experimental tests using a rotary platform equipped with several sensors with axes of rotation coincide with orientation of the rigid body. Also, tests for comparison with a linear complementary filter are given.

The optimal control for anti-lock system proposed in this paper consists to search the wheel slip corresponding to the maximum of the tyre-road characteristic curve. This allows maximising the braking tyre forces. In this strategy of control the wheel slip is not imposed but used an algorithm to generated desired optimal wheel sleep. This algorithm is based on a nominal Pacejka tyre model which uses two parameters to give an updated of the tyre model with respect to the real tyre force characteristic. The proposed control method is verified through one-wheel simulation model with a "magic formula" tyre model. Simulations results show the effectiveness of this controller scheme.

The goal of this study consists to design of a control law in order to optimize the braking/traction force. To maximize this force, it must to localize the wheel slip which corresponds to the maximum tire road adhesion characteristic. In the strategy of the control proposed here, the wheel slip control reference is not imposed but this reference would be generated to reach the optimal wheel slip corresponding to the maximum of the tire road characteristic curve. The generation of the control reference is based of the use of a nominal Pacejka tyre model. To adjust this curve around a maximum of real adhesion curve, the model is rewrite in function of two variables which would be estimated. The proposed control method is verified through one-wheel simulation model with a "magic formula" tire model. Simulations results show the effectiveness of this controller scheme