Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

The synergetic control technique (SCT) has the solution for understanding the symmetry inherent in the non-linear properties of wind turbines (WTs); therefore, they achieve excellent performance and enhance the operation of the WT. Small-scale WTs are efficient and cost-effective; they are usually installed close to where the generated electricity is used. This technology is gaining popularity worldwide for off-grid electricity generation, such as in rural homes, farms, small factories, and commercial properties. To enhance the efficiency of the WT, it is vital to operate the WT at its maximum power. This work proposes an efficient and fast maximum power point tracking (MPPT) technique based on the SCT to eradicate the drawbacks of the conventional methods and enhance the operation of the WT at the MPP regardless of wind speed and load changes. The SCT has advantages, such as robustness, simplified design, fast response, no requirement for knowledge of WT characteristics, no need for wind sensors or intricate power electronics, and straightforward implementation. Furthermore, it improves speed convergence with minimal steady-state oscillations at the MPP. The investigated configuration involves a wind-driven permanent magnet synchronous generator (PMSG), uncontrolled rectifier, boost converter, and variable load. The two converters are used to integrate the PMSG with the load. Three scenarios (step changes in wind speed, stochastic changes in wind speed, and variable electrical load) are studied to assess the SCT. The results prove a high performance of the suggested MPPT control method for a fast convergence speed, boosted WT efficacy, low oscillation levels, and applicability under a variety of environmental situations. This work used the MATLAB/Simulink program and was then implemented on a dSPACE 1104 control board to assess the efficacy of the SCT. Furthermore, experimental validation on a 1 kW Darrieus-type WT driving a PMSG was performed.

In this paper, a nonlinear control technique based on the property of flatness is proposed to control the diesel engine air system. More precisely, to achieve tracking of suitable references (corresponding to low emissions) for the air-fuel ratio and the fraction of the recirculated exhaust gas. The simulated diesel engine is a six cylinders medium duty Caterpillar 3126 B equipped with a variable geometry turbocharger and an exhaust gas recirculation valve. The proposed controller is designed on the reduced third order mean value model and implemented by endogenous linearizing dynamic feedback on the full order model. The controller is assessed through simulations with a SIL architecture using dSpace simulator. It exhibits good control performance without zero dynamics and ensures global stability and tracking of flat output references.

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

In this paper we apply dynamic feedback linearization to the tracking problem for a turbocharged diesel engine (TDE) equipped with exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT). The model used here is the third-order mean-value model, a reduction of the eighth-order mean-value one, see, for sake of simplicity. Our goal is to track desired values of suitably chosen outputs. In fact, we first plan to control the input manifold pressure and the compressor mass flow rate instead of the air fuel ratio (AFR) and the EGR fraction. Unfortunately, the former lead to a non-minimum phase system while the latter are not accessible for measurements in a vehicle, see. We thus replace the problem of tracking of desired values of the original output y (input manifold pressure and compressor mass flow rate) by that of tracking a suitably constructed modified output for which the values to be tracked are specifically chosen: namely, when the modified output y tilde approaches them, the original output converges to the desired values. Simulation results are presented.

Recent research in intelligent transportation system focus on different possible use cases. Among them are the payment applications with cooperative intelligent transport system (C-ITS) equipment. In this paper, we deal about the tolling application performed with the C-ITS equipment. These latter use the ITS-G5 technology, the Roadside Unit (RSU) and the on-board unit (OBU) with features specified by the standardization Institute ETSI. To perform this service, we highlight two mains requirements: how to have a reliable geolocation of the vehicle when it crosses the tollgate and how to secure the communication during the transaction process. In this paper, we will address the methods that is propose to solve the issue of geolocation. The proposed method takes advantage of the communications data. It uses the principle of Differential - GPS mixed with Kalman filtering and extended Kalman filtering applied to the GPS and CAN bus measurements received by the RSU from the connected vehicles. The kinematic of the vehicle is taken into account to choose the suitable system model. Here as novelties, we discuss about the whole procedure to perform the geolocation process during the tolling service and we have results when we apply the dynamic model according to the real behavior of the vehicle when approaching and crossing the toll station.