The need to achieve high performance with very high control accuracy in an electrical machine is paramount and has led to increased interest in modern motor control techniques. Modern motor techniques often require position sensors to be integrated into electrical machines. The position estimation approach offers a cost advantage within acceptable error results over the position measurement approach, making the position estimation approach more suitable. This paper deals with sensorless position control of a synchronous reluctance machine (SynRM) by applying the load torque of an electric vehicle drive train based on an extended Kalman filter (EKF). The developed observer could be an excellent replacement for the analogue position sensor. This work is done in two steps: Firstly, the EKF observer is synthesised and secondly, two types of controllers are used for velocity and currents; the PI and the super-twisting controllers. The simulation results obtained in this work confirm the accuracy and reliability of the developed observer with the super-twisting controllers.

This work aims at solving the torque ripple effect problem minimization in synchronous reluctance machines. The objective is realized by controlling the currents in the synchronous reluctance machine in respect to the reference current and as well, leads to the control of the torque of the machine thereby; reducing the torque ripple effect. In the first, Field -Oriented Control (FOC) is used to control the current of the machine in respect to the reference current. In the second, the Maximum Torque Per Ampere MTPA as well as the Optimal Current control approach are applied for the same purpose. The obtained results are analyzed and the best architecture is selected with the use of a Proportional Integral (PI) controller.

A Passive Fault Tolerant Control (PFTC) based on deep Reinforcement Learning (RL) approach has been proposed in this work. The RL agent is responsible for controlling a heating system in order to regulate the indoor temperature of an area. During operation, faults may occur in the heating system, particularly at the level of the internal resistance of the boiler and the pump responsible for the flow of water injected into the pipes to heat the area. The RL agent has been trained without the presence of a fault either in a holy environment and the goal is to see his behavior when the fault appears. The agent manages even without prior training to adapt and propose a PFTC to guarantee an interior temperature faithful to the set temperature while ensuring minimizing the energy consumed and good use of the heating system to guarantee a long equipment life.

Energy consumption in buildings has become a real concern for scientists and seeking to reduce this consumption is essential. Heating, ventilation, and air conditioning (HVAC) systems account for more than 50% of this consumption. One of the solutions to reduce this excessive consumption is to detect and diagnose faults that can appear instantaneously and quickly with fault diagnostic detection systems (FDD) based on artificial intelligence. The paper presents a strategy based on a data-driven approach for the detection and diagnosis of sensor faults that may appear in the Air Handling Unit (AHU) systems. A Decision Tree, Random Forest and SVM algorithm were used to detect and diagnose temperature sensor faults occurring in the AHU. The comparison between these methods shows that the Random Forest gives the best result with 96% accuracy.

Currently, with the explosive growth of the Internet of Things (IoT) technologies and systems, an increasing number of practical applications can be found in many fields including industry, asset tracking, agriculture, smart metering, smart city and building. IoT sensors based on Low-Power Wide-Area (LPWA) Network technologies appears suitable for the deployment of instruments and sensors on a traditional telecom tower to develop additional services for tower Company allowing a better monitoring of its facilities, energy consumption and various equipment supervision. As many telecom sites are close to cities, they can also contribute to develop services for the future smart city and building needs. However, this feasibility must be performed in a real use of Telecom Tower conditions, such as frequency interference. In this paper, we propose a method to equip a telecom tower considering the Long Range Wide Area Network (LoRaWAN) protocol in order to gather data from IoT sensors networks that are deployed in the surrounding buildings. We show that the use of the top of telecom tower allows a large private LoRaWAN network covering up to 32 km with only one gateway. Data from different sensors are retrieved directly on local computers and on Cloud applications for further processing. Dashboards are then realized from IoT platforms for monitoring and energy management of telecom towers which can be extended to the buildings smart supervision and control.

In high-speed milling process, the early detection of chatter contains two aspects, one is the fast detection of the initial symptom of the chatter, the other is to recognize the slight chatter embedded in the noisy signal. This paper presents an online cumulative chatter detection method for high-speed milling process based on once-per-revolution sampling, maximum entropy (MaxEnt) principle and sequential probability ratio test (SPRT). The method allows for coping with these two aspects of early chatter detection. This method has less computational complexity and is independent of the cutting conditions. The procedure consists of four steps. First, the prior knowledge of early chatter is determined. Secondly, once-per-revolution sampling data is sampled from the vibration signal. Thirdly, the MaxEnt principle is used to estimate the MaxEnt of the once-per-revolution sampling data as a chatter indicator. Finally, the SPRT cumulates the information of the estimated MaxEnt, and then detect the early chatter by using the prior knowledge. The proposed strategy is applied to a high-speed milling process, and two simulation experiments allowed to assess the effectiveness of the early chatter detection method.

This paper proposes an approach of hybrid Petri nets modeling for hybrid renewable energy production systems structured in micro-grids. This approach is original in the sense that it enables to study the behavior of such systems under various reconfiguration constraints in order to fulfill energy demands for customers. The proposed Petri net model allows to considering the discreet events related to the availability of energy resources or to components' failures in the system as well in the discreet reconfiguration (structural or functional), for control strategies specifications. In addition, an operational research model for cost minimization and availability maximization through reconfiguration is also proposed. The joint implementation of both models should lead to a new approach of design of hybrid renewable energy systems and of their effective cost optimization.

This paper presents a framework of multi-model based approach for interconnected components network by finite state machine model. The main concept on which we base our approach is the fact that, in a context of interconnected components, due to the influence of some components named here key-components the system can switch from one mode to another. The influence of these components can be measured by the level of involvement in the switching events. The proposed approach can be applied to embedded systems in which we propose to consider the controller as a part of the system.

This paper presents a Finite State Machine approach of grid system, for tracking the state of involved components in a given behavior of this system. In large system context, classic methods such as state composition become time consuming and cumbersome due to the explosion problem of combinatorial states. The approach presented in this paper tend to overcome this problem, using finite state machines constructed with median symmetry operators as transition alphabet, and matrices of local components state as vertices.

This paper considers the allocation of reliability and redundancy to parallel-series systems, while minimizing the cost of the system. It is proven that under usual conditions satisfied by cost functions, a necessary condition for optimal reliability allocation of parallel-series systems is that the reliability of the redundant components of a given subsystem are identical. An optimal algorithm is proposed to solve this optimization problem. This paper proves that the components in each stage of a parallel-series system must have identical reliability, under some nonrestrictive condition on the component's reliability cost functions. This demonstration provides a firm grounding for what many authors have hitherto taken as a working hypothesis. Using this result, an algorithm, ECAY, is proposed for the design of systems with parallel-series architecture, which allows the allocation of both reliability and redundancy to each subsystem for a target reliability for minimizing the system cost. ECAY has the added advantage of allowing the optimal reliability allocation in a very short time. A benchmark is used to compare the ECAY performance to LM-based algorithms. For a given reliability target, ECAY produced the lowest reliability costs and the optimum redundancy levels in the successive reliability allocation for all cases studied, viz, systems of 4, 5, 6, 7, 8, 9 stages or subsystems. Thus ECAY, as compared with LM-based algorithms, yields a less costly reliability allocation within a reasonable computing time on large systems, and optimizes the weight and space-obstruction in system design throughout an optimal redundancy allocation.