Nicolae Tudoroiu

Interactive Bank of Unscented Kalman Filters for Fault Detection and Isolation in Reaction Wheel Actuators of Satellite Attitude Control System

The main objective of the research investigated in this paper is the detection and isolation of partial (soft) and total (hard) failures in the reaction wheel (RW) actuators of the satellite attitude control system (ACS) during its mission operation. The fault detection and isolation (FDI) is accomplished using the interactive multiple models (IMM) scheme developed based on the unscented Kalman filter (UKF) algorithm. Towards this objective, the healthy mode of the ACS system under different operating conditions as well as a number of different fault scenarios including changes and anomalies in the temperature, power supply bus voltage, and unexpected current variations in the actuators of each axis of the satellite are considered. We describe and develop a bank of interacting multiple model unscented Kalman filters (IMM-UKF) to detect and isolate the above mentioned reaction wheel failures in the ACS system. Also, it should be emphasized that the proposed IMM-UKF technique is implemented based on a high-fidelity highly nonlinear model of a commercial RW. Compared to other fault detection and isolation (FDI) strategies developed in the control systems literature, the proposed FDI scheme is shown, through extensive numerical simulations, to be more accurate, less computationally demanding, and more robust with the potential of extending to a number of other engineering applications

Fault Detection and Diagnosis (FDD) in Heating Ventilation Air Conditioning Systems (HVAC) Using an Interactive Multiple Model Augmented Unscented Kalman Filter (IMMAUKF)

Nowadays monitoring and controlling the modern and sophisticated heating ventilation air conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions is becoming a difficult and challenging task. Their complexity drastically increases and the control becomes more difficult task due to the several control loops that interact between them. Among these control loops the discharge air temperature (DAT) loops, the static pressure loop (SP), and the variable air volume (VAV) terminal unit loop are the candidate loops requiring frequent re-tuning. Equipment failures and loss of control leading to less than acceptable indoor environment conditions is a common problem reported in these systems. In our paper we consider the degradation in the DAT loop performance caused by a gradual increase in backlash of the valve actuator and several disturbances that occur in the process. The main objective of this paper is to describe the application of an interactive multiple model (IMM) [9] based on an augmented unscented Kalman filter (UKF) estimation algorithm (IMMAUKF) [2] to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in DAT loop of the HVAC systems [4]. The proposed algorithm is an alternative to the interactive multiple model (IMM) developed in the literature based on the extended Kalman filter standard technique [2], [3]-[5], [7]-[11], the most popular estimation technique used in the last 40 years. The main advantage of the proposed algorithm is the less computation, consequently faster, high accuracy, robustness and eliminates completely the linearization of the system dynamics.

Real-Time Sliding Mode Observer Estimator Integration in Hybrid Electric Vehicles Battery Management Systems

In this paper we develop and implement a real-time sliding mode observer estimator (SMOE) for state-of-charge (SOC) and for current fault in Li-Ion batteries packs integrated in the battery management systems (BMS) structure of hybrid electric vehicles (HEVs). The estimation of SOC is critical in automotive industry for successful marketing of both electric vehicles (EVs) and hybrid electric vehicles (HEVs). Gradual capacity reduction and performance decay can be evaluated rigorously based on the current knowledge of rechargeable battery technology, and consequently is required a rigorous monitoring and a tight control of the SOC level, necessary for increasing the operating batteries lifetime. The novelty of this paper is that the proposed estimator structure can be also tailored to estimate the SOC and the possible faults that could occur inside of the batteries of different chemistry by augmenting the dimension of the model states, according to the number of estimated battery faults. The preliminary results obtained in this research are encouraging and reveal the effectiveness of the real-time implementation of the proposed estimator in a MATLAB/SIMULINK programming simulation environment.

Real-time implementation of DC servomotor actuator with unknown uncertainty using a sliding mode observer

The central idea of this paper is the modeling and implementation of a real-time dc servomotor angular speed control system with an unknown bounded uncertainty using a sliding mode observer (SMO) control strategy. We prefer to use a SMO in our approach due to its great potential in fault detection and isolation (FDI) of the actuators and sensor faults subjected frequently to several failures due to an abnormal change in their operating conditions or parameters. We use for this purpose the most suitable real time implementation tool MATLAB/SIMULINK software package. It provides special features for real time implementation by its extensions Real-Time Workshop (RTW) and the Real-Time Windows Target (RTWT). The novelty of our paper is to prove in an extensive simulation MATLAB/SIMULINK frame the real time implementation potential of a most recently sliding mode observer (SMO) control strategy applied to a particular case study, namely for a dc servomotor angular speed control system. The proposed real-time Sliding Mode Observer (SMO) consists of an embedded nonlinear Sliding Mode Observer (SMO) with the dc servomotor actuator in an integrated control system structure to estimate its angular speed and armature current and to implement the sliding mode control law.

The neural simulator of the pollution factors impact on the quality of the water inside the Romanian harbour of Black Sea

The aim of this research is to identify and investigate the pollution sources inside the Romanian harbor of Black Sea from Constanta, by monitoring the quality water parameters. Furthermore we will try to disseminate the information collected and we will propose adequate actions to prevent the continuous degradation of the environment. Several sampling sites were established around the Port zone and analyses were carried monthly. Physical and chemical indicators of the water quality, nutrients compounds, Dissolved Oxygen (DO), petroleum/hydrocarbons pollution indicators, Salinity, Sulfides, Cu, Cr and Fe are the monitored quality parameters. The values of quality-monitored parameters are variable in quasi-large ranges, depending on the position of the sampling sites. A direct correlation between these indicators can be done. Water pollution causes loss of biomass, affecting some organisms until they disappear and also blooms of phytoplankton destroying the ecosystems, due to the loss of oxygen from the water. The most important measures to remediate the area were building dams, cleaning residual water, building areas of protected water, and building special places to collect the residues, to prevent direct discharge in the surface water. In our research we try to make an interesting presentation of the neural simulation model of the wide database concerning the water pollution degree inside of Constanta port strategic location, and based on this, to predict the future results.

The neural simulator of the pollution factors impact on the quality of the air along the Romanian coast of Black Sea

The aim of this research is to identify and investigate the pollution sources along the Romanian coast of Black Sea, especially in Constanta neighbourhood, by monitoring the quality air parameters. Furthermore we will try to disseminate the information collected and we will propose adequate actions to prevent the continuous degradation of the environment. Several sampling sites were established around and inside the Constanta city resort, and analyses were carried monthly. Physical and chemical parameters of the air quality, such as temperature, wind speed, Carbon Dioxide (C02), methane (CH4), Nitrogen Oxide, ozone, water vapours concentrations are monitored “in situ” in different sampling sites of the Romanian Black Sea coast‥ The values of quality-monitored parameters are variable in quasi-large ranges, depending on the position of the sampling sites. A direct correlation between these indicators can be done. Air pollution causes the “greenhouse effect” with a high impact on the live and fauna, destroying the Black Sea ecosystems. The most important measures to remediate the area were more efficient filters installation in the industrial area, cleaning residual water, and building special places to collect the residues, to prevent direct discharge in the surface water. In our research we try to make an interesting presentation of the neural simulation model of the wide database concerning the air pollution degree inside Constanta resort city, and based on this, to predict the future results.

MIMO stochastic minimum variance control strategies for improving plasma characteristics in Reactive Ion Etching

In this paper, we propose to use multi input multi-output (MIMO) stochastic minimum variance control strategy to control the plasma characteristics in Reactive Ion Etching (RIE) process. Given the RIE model in the state-space representation a stochastic adaptive minimum variance tracking controller (SMVAC) is designed and tuned to control the key plasma parameters. For the stochastic adaptive minimum variance approach a system identification scheme based on the minimum variance principle is integrated with a control strategy derived from minimization of a per-interval performance index. This control strategy performs very well, has good accuracy, as demonstrated in simulation environment, and can easily be applied to MIMO RIE system. The principal idea is that by controlling appropriate key plasma parameters (the concentrations of the reactive radicals and ions and ions energy) it is possible to improve the etch performance of the reactive ion etchers, namely their selectivity, uniformity, anisotropy and etch depth. The proposed real-time control strategies reveal superior accuracy and performance when compared to the results available in the plasma literature.

Unscented Kalman Filter (UKF) and frequency analysis (FA) techniques used for fault detection, diagnosis and isolation (FDDI) in Heating Ventilation Air Conditioning systems (HVAC)-comparison results

Monitoring and controlling the modern and sophisticated Heating Ventilation Air Conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions represents a difficult and challenging task. Their complexity drastically increases and the control becomes more difficult task due to the several control loops that interact between them. The main objective of this study is to compare the performance of the automated strategies for fault detection, diagnosis and isolation (FDDI) based on frequency and spectral analysis (FA) of the system response, and an interactive multiple model (IMM), based on the Unscented Kalman Filter (UKF) estimation technique to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in Discharge air temperature (DAT) loop of the HVAC systems. The both techniques are HVAC model-driven based and the simulations results reveal the superiority of the Interactive Multiple Model based on Unscented Kalman Filter estimation algorithm (IMM_UKF) concerning its accuracy and robustness to the changes in the system structure parameters. These algorithms are implemented in a simulation environment, and the fault diagnosis results are presented for a several fault scenarios in terms of mode probabilities and active fault index. From the preliminaries simulations, for different scenarios we found that the IMM_UKF algorithm is robust to the choice of the matrix probability and to the small changes in process and measurement noise level, result that is confirmed in the literature.

An Adaptive Observer State-of-Charge Estimator of Hybrid Electric Vehicle Li-Ion Battery - A Case Study

In this research paper we investigate the procedure design and the implementation in a real time MATLAB SIMULINK R2017a simulation environment of an accurate adaptive observer state estimator. The effectiveness of the observer state estimator design is proved through intensive simulations performed to estimate the state-of-charge of a lithium-ion rechargeable battery integrated in a hybrid electric vehicle Battery Management System structure for a particular Honda Insight Japanese car. The state-of-charge is an essential internal parameter of the lithium-ion battery, but not directly measurable, thus an accurate estimation of battery state-of-charge becomes a vital operation for the Battery Management System. This is the main reason that motivates us to find the most suitable state-of-charge estimator in terms of estimation accuracy, fast convergence and robustness to the possible changes in the state-of-charge initial value, to the temperature effects on the battery, changes in the battery internal resistance and nominal capacity.

1-D Wavelet Signal Analysis of the Actuators Nonlinearities Impact on the Healthy Control Systems Performance

A R T I C L E I N F O A B S T R A C T Article history: Received: 01 June, 2017 Accepted: 11 July, 2017 Online: 04 September, 2017 The objective of this paper is to investigate the use of the 1-D wavelet analysis to extract several patterns from signals data sets collected from healthy and faulty input-output signals of control systems as a preliminary step in real-time implementation of fault detection diagnosis and isolation strategies. The 1-D wavelet analysis proved that is a useful tool for signals processing, design and analysis based on wavelet transforms found in a wide range of control systems industrial applications. Based on the fact that in the real life there is a great similitude between the phenomena, we are motivated to extend the applicability of these techniques to solve similar applications from control systems field, such is done in our research work. Their efficiency will be demonstrated on a case study mainly chosen to evaluate the impact of the uncertainties and the nonlinearities of the sensors and actuators on the overall performance of the control systems. The proposed techniques are able to extract in frequency domain some pattern features (signatures) of interest directly from the signals data set collected by data acquisition equipment from the control system.