In this paper, a bond graph fault diagnosis methodology applied to a three-phase inverter is presented. A qualitative approach is used, which is based on signal amplitude behavior analysis when a fault in the system is presented. For a complete bond graph modelling of the three-phase inverter, sub-models for each power switches are used. Considerations about switching devices from the bond graph point of view are made in order to cope the simulation aspect and, at the same time, to avoid causality problems in the qualitative diagnosis approach employed. We include both open-circuit and short-circuit faults on power switches.

A real-time localization algorithm is presented in this paper. The algorithm presented here uses an extended Kalman filter and is based on Time Difference Of Arrivals (TDOA) measurements of radio signal. The position and velocity of an Unmanned Aerial Vehicle (UAV) are successfully estimated in closed-loop in real-time, both in hover and path following flights. Relatively small position errors obtained from the experiments prove the good performance of the proposed algorithm.

This paper proposes the study of a robust control scheme for an Unmanned Surface Vehicle (USV) in a urban waterway using Visual Odometry (VO) for position estimation of the vehicle instead of traditional sensors. For different applications, a USV is required to operate in autonomous navigation mode and under various disturbances inherent to its environment as currents and waves. To achieve efficient operation, a robust control scheme is required to cope with such disturbances. The autonomous navigation is achieved by using VO to estimate the vehicle’s position for denied Global Positioning Systems (GPS) environments. A robust control scheme based on Sliding Modes (SM) control theory is designed and its convergence is guaranteed by means of Lyapunov analysis. The tracking capabilities under disturbance conditions using VO for position estimation are verified in simulation using the virtual world tool from Matlab and Simulink.

This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.