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EN
Safety-critical and mission-critical systems are often sensitive to functional degradation at the system or component level. Such degradation dynamics are often dependent on system usage (or control input), and may lead to significant losses and a potential system failure. Therefore, it becomes imperative to develop control designs that are able to ensure system stability and performance whilst mitigating the effects of incipient degradation by modulating the control input appropriately. In this context, this paper proposes a novel approach based on an optimal control theory framework wherein the degradation state of the system is considered in the augmented system model and estimated using sensor measurements. Further, it is incorporated within the optimal control paradigm leading to a control law that results in deceleration of the degradation rate at the cost of system performance whilst ensuring system stability. To that end, the speed of degradation and the state of the system in discrete time are considered to develop a linear quadratic tracker (LQT) and regulator (LQR) over a finite horizon in a mathematically rigorous manner. Simulation studies are performed to assess the proposed approach.
EN
In time series analysis, signal processing, and financial analysis, simple moving average (SMA), weighted moving average (WMA), exponential moving average (EMA), exponential weighted moving average (EWMA), and Kalman filter are widely used methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific application and data characteristics. It is important for researchers and practitionersto understand the properties and limitations of these methods in order to make informed decisions when analyzing time seriesdata. This study investigates the effectiveness of time series analysis methods using data modeled with a known exponential function with overlaid random noise. This approach allows for control of the underlying trend in the data while introducing the variability characteristic of real-world data. The relationships were written using scripts for the construction of dependencies, and graphical interpretation of the results is provided.
PL
W analizie szeregów czasowych, przetwarzaniu sygnałów i analizie finansowej szeroko stosowane są: prosta średnia ruchoma (SMA), ważona średnia ruchoma (WMA), wykładnicza średniaruchoma (EMA), wykładniczo-ważona średnia ruchoma (EWMA) i filtr Kalmana. Każda z metod ma swoje mocne i słabe strony, a wybór metody zależy od konkretnego zastosowania i charakterystyki danych. Dla badaczyi praktyków ważne jest zrozumienie właściwości i ograniczeń tych metod w celu podejmowania świadomych decyzji podczas analizy danych szeregów czasowych. W niniejszej pracy zbadano skuteczność metod analizy szeregów czasowych z wykorzystaniem danych modelowanych znaną funkcją wykładniczą z nałożonym szumem losowym. Takie podejście pozwala na kontrolowanie głównego trendu w danych przy jednoczesnym wprowadzeniu zmienności typowej dla danych rzeczywistych. Do budowy zależności zostały napisane skrypty. Podanajest graficzna interpretacja wyników.
EN
This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.
EN
The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.
5
PL
W artykule przedstawiono wyniki prac nad projektem dwukołowej samobalansującej platformy mobilnej przygotowanym w ramach pracy inżynierskiej. Celem stworzenia konstrukcji było umożliwienie zwiększania mobilności osoby dorosłej na niewielkich odległościach w zurbanizowanym środowisku. Całość prac projektowych podzielono na kilka części. W pierwszym etapie przedstawiono wymagania założone dla projektu urządzenia, dobrano elementy elektryczne oraz schemat ich połączeń elektrycznych. W drugiej części omówiono stworzony model CAD konstrukcji oraz jego elementy mechaniczne. W celu sprawdzenia wytrzymałości konstrukcji dokonano analizy MES korpusu urządzenia. Przedostatnią częścią było przeanalizowanie zagadnienia odwróconego wahadła, które pozwoliło na wyprowadzenie modelu przestrzeni stanu z rozdzieleniem na podsystemy bazy i drążka sterowniczego niezbędnego do opracowania sterowania dla platformy. W ostatniej części przygotowano algorytm stabilizujący na podstawie regulatora LQR oraz rozważano zastosowanie fuzji sensorycznej w postaci filtru Kalmana w celu zwiększenia dokładności określania kąta odchylenia konstrukcji. Na koniec przygotowano symulacje w środowisku Simulink w celu sprawdzenia poprawności przygotowanego algorytmu. Całość została zwieńczona podsumowaniem prac oraz wytyczeniem kierunków dalszych badań.
EN
The article presents the results of work on a project for a two-wheeled self-balancing mobile platform prepared as part of engineering work. The purpose of creating the structure was to enable the increased mobility of an adult over short distances in an urbanized environment. The whole design work was divided into several parts. In the first stage, requirements were assumed for the design of the device, electrical elements chosen, and a diagram of their electrical connections is presented. In the second part, the created CAD model of the structure is presented and some of mechanical elements described. In order to check the strength of the structure, the FEM analysis of the device body was carried out. The penultimate part was to analyze the problem of the inverted pendulum, which allowed to separate the state space model into a base subsystem and a control stick subsystem necessary to develop the control for the platform. In the last part, a stabilizing algorithm based on the LQR regulator was prepared and the use of sensory fusion in the form of a Kalman filter was focused on in order to increase the accuracy of determining the angle of deflection of the structure. Finally, simulations were prepared in the Simulink environment in order to check the correctness of the prepared algorithm. The whole was crowned with a summary of the work and setting directions for further research.
6
Content available Mechatronic design of a two-wheeled mobile platform
EN
The article presents the results of work on a project for a two-wheeled self-balancing mobile platform prepared as part of engineering work. The purpose of creating the structure was to enable the increased mobility of an adult over short distances in an urbanized environment. The whole design work was divided into several parts. In the first stage, requirements were assumed for the design of the device, electrical elements chosen, and a diagram of their electrical connections is presented. In the second part, the created CAD model of the structure is presented and some of mechanical elements described. In order to check the strength of the structure, the FEM analysis of the device body was carried out. The penultimate part was to analyze the problem of the inverted pendulum, which allowed to separate the state space model into a base subsystem and a control stick subsystem necessary to develop the control for the platform. In the last part, a stabilizing algorithm based on the LQR regulator was prepared and the use of sensory fusion in the form of a Kalman filter was focused on in order to increase the accuracy of determining the angle of deflection of the structure. Finally, simulations were prepared in the Simulink environment in order to check the correctness of the prepared algorithm. The whole was crowned with a summary of the work and setting directions for further research.
PL
W artykule przedstawiono wyniki prac nad projektem dwukołowej samobalansującej platformy mobilnej przygotowanym w ramach pracy inżynierskiej. Celem stworzenia konstrukcji było umożliwienie zwiększania mobilności osoby dorosłej na niewielkich odległościach w zurbanizowanym środowisku. Całość prac projektowych podzielono na kilka części. W pierwszym etapie przedstawiono wymagania założone dla projektu urządzenia, dobrano elementy elektryczne oraz schemat ich połączeń elektrycznych. W drugiej części omówiono stworzony model CAD konstrukcji oraz jego elementy mechaniczne. W celu sprawdzenia wytrzymałości konstrukcji dokonano analizy MES korpusu urządzenia. Przedostatnią częścią było przeanalizowanie zagadnienia odwróconego wahadła, które pozwoliło na wyprowadzenie modelu przestrzeni stanu z rozdzieleniem na podsystemy bazy i drążka sterowniczego niezbędnego do opracowania sterowania dla platformy. W ostatniej części przygotowano algorytm stabilizujący na podstawie regulatora LQR oraz rozważano zastosowanie fuzji sensorycznej w postaci filtru Kalmana w celu zwiększenia dokładności określania kąta odchylenia konstrukcji. Na koniec przygotowano symulacje w środowisku Simulink w celu sprawdzenia poprawności przygotowanego algorytmu. Całość została zwieńczona podsumowaniem prac oraz wytyczeniem kierunków dalszych badań.
EN
Nowadays, there is still a need for the development of a high-precision vibration measurement system for aircraft wings. By analyzing the wing vibration characteristics a lot of aviation studies could be conducted, including the wing health monitoring, the fluttering phenomenon and so on. This paper presents preliminary results of the research carried out toward building a promising system designed to measure vibration parameters of aircraft wing. Comparing it with the existing analogue systems, the proposed system features the use of approaches that are traditional for solving orientation and navigation problems for vibration measurements. The paper presents the basic structure of the system, the fundamentals of its operation, the mathematical errors models of its main components, the correction algorithms using optimal Kalman filter. Finally, the initial simulation results of system operation are shown, demonstrating the expected accuracy characteristics of the system, which confirms its effectiveness and the prospects of the chosen direction of research.
EN
This paper deals with the problem of joint state and unknown input estimation for stochastic discrete-time linear systems subject to intermittent unknown inputs on measurements. A Kalman filter approach is proposed for state prediction and intermittent unknown input reconstruction. The filter design is based on the minimization of the trace of the state estimation error covariance matrix under the constraint that the state prediction error is decoupled from active unknown inputs corrupting measurements at the current time. When the system is not strongly detectable, a sufficient stochastic stability condition on the mathematical expectation of the random state prediction errors covariance matrix is established in the case where the arrival binary sequences of unknown inputs follow independent random Bernoulli processes. When the intermittent unknown inputs on measurements represent intermittent observations, an illustrative example shows that the proposed filter corresponds to a Kalman filter with intermittent observations having the ability to generate a minimum variance unbiased prediction of measurement losses.
EN
The main goal of estimating models for industrial applications is to guarantee the cheapest system identification. The requirements for the identification experiment should not be allowed to affect product quality under normal operating conditions. This paper deals with ensuring the required liquid levels of the cascade system tanks using the model predictive control (MPC) method. The MPC strategy was extended with the Kalman filter (KF) to predict the system’s succeeding states subject to a reference trajectory in the presence of both process and measurement noise covariances. The main contribution is to use the application-oriented input design to update the parameters of the model during system degradation. This framework delivers the least-costly identification experiment and guarantees high performance of the system with the updated model. The methods presented are evaluated both in the experiments on a real process and in the computer simulations. The results of the robust MPC application for cascade system water levels control are discussed.
EN
Monitoring decadal shoreline change is essential to understand the influence of coastal processes on the coastline. The shoreline is constantly shaped by natural and anthropogenic factors, and so, it is critical to understand decadal trends. The prediction of future shoreline positions is a must for effective long-term coastal zone management. This study was conducted along a 90-km-stretch of the coastline from the mouth of the Haldi River (Purba Medinipur) in the Northeast to the Subarnarekha estuary (Balasore) in the Southwest. The primary objectives of the study were to analyze the decadal shoreline migration using the End Point Rate (EPR) method and then predict future shoreline change prediction using the Kalman Filter method. Shoreline positions were digitized after extracting the shorelines using Principal Component Analysis (PCA) from Multi-temporal (1990, 2000, 2010, and 2020) and Multisensor (Landsat TM, ETM+, and OLI) satellite data. A total of 887 transects were cast to compute change statistics of the time series shoreline. It was observed that the average shoreline change rate was −8.41 m/year in the periods of 1990–2000 and 2000–2010, and −8.80 m/year from 2010 to 2020. Accretion along this coastal stretch is caused by the growth of morphological features such as sand bars, beaches, and dunes. We also found that erosion occurred from 1990 to 2000 along the coastline of Bhograi, Ramnagar-I, Ramnagar-II, a few parts of Contai-I, Khejuri-I, and the Nandigram-I coastal block. Accretion mostly occurred due to Land reclamation in the Northern portion of Bhograi, Contai-1 blocks and Nandigram- I block from 2000 to 2010 and 2010 to2020. Root mean square error (RMSE) and Regression Coefficient values were computed for the future shoreline prediction of 2031 and 2041. The calculated RMSE value of±4.7 m and value of 0.97 shows a good relationship between the actual and predicted coastline of 2020. This study concludes that the coastline of Purba Medinipur-Balasore experienced severe erosion and needs management action and also proves the efficiency of the Digital Shoreline Analysis System (DSAS) tool for decadal analysis and prediction of shoreline change. The findings of this study may help the coastal planners, environmentalists, and coastal managers in preparing both short-term and long-term coastal zone management plans.
EN
This paper presents a comparison between LQGi and LQGi/LTR control of a Twin Rotor MIMO System (TRMS)(made by feedback instrument company). The Linear Quadratic Gaussian (LQG) means a linear quadratic regulator with KALMAN filter. LQG/LTR is an LQG controller with Loop Transfer Recovery. For both we introduced an integrator to deal with steady state errors and disturbances. A feed forward controller has been added to improve the tracking performances of the system. The results are in simulation and in real time.
PL
Przedstawiono porównanie sterowania LQG I LQG/LYR systemu MIMO z dwoama wirnikami TRMS. W sterowaniu LQG wykorzystano filtr Kalmana. Dodatkowo wprowadzono układ całkujący oraz sterownik typu feed forward. Sterowanie analizowano przez symulacje oraz eksperymentalnie.
12
Content available remote Highly nonlinear systems estimation using extended and unscented kalman filters
EN
The main idea of this study is to evaluate the estimation performance of extended and unscented Kalman filters (EKF and UKF). So, these latter are introduced to estimate the dynamic states of a similar model operating with identical covariance matrices in the same situation. The mean square error (MSE) criterion is used to quantify the estimation error between the actual and the estimated values. The simulation results obtained with Matlab/ Simulink software confirm the superiority and efficiency of UKF over EKF, especially when the system is highly non-linear under process and measurement noises, such is the case of the inverted double pendulum mounted on a cart (DIPC).
PL
Główną ideą tego badania jest ocena wydajności estymacji rozszerzonych filtrów Kalmana (EKF i UKF). Te ostatnie zostały wprowadzone w celu oszacowania stanów dynamicznych podobnego modelu działającego z identycznymi macierzami kowariancji. Kryterium błędu średniokwadratowego (MSE) służy do ilościowego określenia błędu oszacowania między wartościami rzeczywistymi i szacunkowymi. Wyniki symulacji uzyskane za pomocą oprogramowania Matlab i Simulink potwierdzają wyższość i wydajność UKF nad EKF, zwłaszcza gdy system jest wysoce nieliniowy
13
Content available remote Electric vehicle yaw moment control based on the body sideslip estimation
EN
The direct yaw moment control (DYC) presents an operative solution to improve the stability and road holding of vehicles, in particular electric vehicles equipped with independent motors. The torque control applied to each wheel can improve the handling performance of a vehicle making it safer and faster and in critical driving situations. In this article, a new method proposed for the control of the direct yaw moment based on the sliding mode control. This method uses a new design of switching function to simultaneously track the desired yaw rate and the side slip of the vehicle. The lateral sideslip angle of the vehicle is estimated by using a Kalman filter. The results of the comparative simulations show the effectiveness of the proposed method with the other conventional methods in terms of following the reference yaw rate, the vehicle trajectory and the vehicle skidding in various difficult driving scenarios.
PL
Bezpośrednie sterowanie momentem odchylenia (DYC) stanowi funkcjonalne rozwiązanie poprawiające stabilność i przyczepność pojazdów, w szczególności pojazdów elektrycznych wyposażonych w niezależne silniki. Kontrola momentu obrotowego zastosowana do każdego koła może poprawić właściwości jezdne pojazdu, czyniąc go bezpieczniejszym i szybszym w krytycznych sytuacjach na drodze. W niniejszym artykule zaproponowano nową metodę sterowania bezpośrednim momentem odchylającym w oparciu o sterowanie trybem ślizgowym. Metoda ta wykorzystuje nową konstrukcję funkcji przełączania do jednoczesnego śledzenia pożądanej wartości odchylenia i poślizgu bocznego pojazdu. Kąt bocznego znoszenia pojazdu jest szacowany za pomocą filtra Kalmana. Wyniki symulacji porównawczych pokazują skuteczność proponowanej metody z innymi metodami konwencjonalnymi w zakresie śledzenia referencyjnego kursu zbaczania, toru jazdy i poślizgu pojazdu w różnych trudnych scenariuszach jazdy.
14
Content available remote An improved real visual tracking system using particle filter
EN
This paper presents a real hybrid visual tracking system based on a special-color model of target with improving the performance of this designed visual tracking system using various linear and nonlinear estimators like Kalman filter and particle filter. Moreover, the whole system has been designed and implemented in the laboratory by fusing the tracking algorithm that was created utilizing python software with a moving camera sensor. In addition, the designed visual tracking system has been simple, low cost and achieved all stages of visual tracking process like target initialization, appearance modeling, movement estimation, and target positioning with a great success. Finally, the graphical analysis results of the designed system illustrated had a great illustration on the validity of utilizing particle filer was very efficient and clearer with maneuvering targets than that were used with Kalman filter.
PL
W niniejszym artykule przedstawiono hybrydowy system śledzenia wizualnego oparty na modelu celu w specjalnej kolorystyce z poprawą wydajności tego zaprojektowanego systemu śledzenia wizualnego przy użyciu różnych liniowych i nieliniowych estymatorów, takich jak filtr Kalmana i filtr cząsteczkowy. Co więcej, cały system został zaprojektowany i wdrożony w laboratorium poprzez połączenie algorytmu śledzenia, który został stworzony przy użyciu oprogramowania Pythona z ruchomym czujnikiem kamery. Ponadto zaprojektowany system śledzenia wizualnego był prosty, tani i osiągnął z dużym sukcesem wszystkie etapy procesu śledzenia wizualnego, takie jak inicjalizacja celu, modelowanie wyglądu, szacowanie ruchu i pozycjonowanie celu. Wreszcie, wyniki analizy graficznej zilustrowanego zaprojektowanego systemu doskonale ilustrują zasadność wykorzystania filtra cząstek, który był bardzo wydajny i wyraźniejszy w przypadku celów manewrujących niż ten, który był używany z filtrem Kalmana.
EN
The vehicular ad-hoc network (VANET) is subject to various attacks because of its dynamic nature and ephemeral character. In VANET, vehicles communicate with each other for safety awareness. The positioning of an unknown vehicle is one of the critical factors to determine the vehicle’s trustworthiness. Although some positioning techniques have achieved a high accuracy level in VANET, they suffer from dynamic noise in real-world environments. This drawback leads to inaccuracy and unreliability during vehicle positioning. In this paper, an optimal innovation based adaptive estimation Kalman filter (OIAE-KF) is proposed. This algorithm offers an alternative solution for the basic Kalman filter and the innovation based adaptive estimation Kalman filter (IAE-KF). The proposed algorithm makes use of fusion of the global navigation satellite system (GNSS) and the inertial measurement unit (IMU) to improve its performance. The OIAE-KF works based on the innovation sequence and involves three steps such as establishing the innovation sequence, applying the innovation property, checking the optimality of the Kalman filter and, finally, estimating process noise (Q) and measurement noise (R). An optimal swapping method is introduced for optimality check. The efficiency of the proposed OIAE-KF method is proved by comparing the predictions of the existing methods such as the IAE-KF. The results show that the OIAE-KF performs better than the existing techniques. It improves the accuracy and consistency in VANET positioning.
EN
This paper proposes a variance upper bound based interval Kalman filter that enhances the interval Kalman filter based on the same principle proposed by Tran et al. (2017) for uncertain discrete time linear models. The systems under consideration are subject to bounded parameter uncertainties not only in the state and observation matrices, but also in the covariance matrices of the Gaussian noises. By using the spectral decomposition of a symmetric matrix and by optimizing the gain matrix of the proposed filter, we lower the minimal upper bound on the state estimation error covariance for all admissible uncertainties. This paper contributes with an improved algorithm that provides a less conservative error covariance upper bound than the approach proposed by Tran et al. (2017). The state estimates are determined using interval analysis in order to enclose the set of all possible solutions of the classical Kalman filter consistent with the uncertainties.
EN
Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.
EN
Stereo photogrammetry has been used in this study to analyse and detect movements within the Lecture theater of School of Environmental Technology of Federal University of Technology Minna via the use of Kalman filter algorithm. The essential steps for implementation of this method are herein highlighted and results obtained indicate Ins. Mov.s (velocity) ranging from ±0.0000001 m/epoch to±0.000007 m/epoch with greater movements noticed in the horizontal direction than in the vertical direction of the building. Because the observed movements were insignificant, the buildinghas been classified as stable. However, a longer period of observation with a bi-monthly observational interval has been recommended to enable decision on the rate of rise/sink and deformation of the building
19
Content available remote Web application service in bus arrival time prediction
EN
Bus arrival time prediction represents very important part of the service that informs passengers of intelligent transport systems in public bus transportation. Different methods are used for the prediction. In this paper, two methods for predicting arrival time of bus are analysed. Proposed method is the freely available Google’s web service “DistanceMatrixAPI” . Comparative view of obtained results using the Kalman filter and Web service is presented. For the experimental research we proposed model of Distribution Modular Information and Communication System. Research results shows that the implementation of Kalman filter method is much more accurate that the use of “DistanceMatrix API” method.
PL
W artykule zaprezentowano inteligentny system informujący pasażerów transportu publicznego o przewidywanym pczasie przyjazdu pojazdu. Analizowano dwie metody. NBajlepsze parametry miał system wykorzystujący filtr Kalmana.
20
Content available remote Error reduction for static localization
EN
This article describes methods for reducing the position measurement error of ultra-wideband localization system - DecaWave TREK1000. The static localization accuracy of this system can achieve 10cm. The localization algorithm introduced in this paper can improve it up to 1 centimeter. We could achieve such good accuracy, thanks to experiments that were carried out in various environmental conditions. This allowed us to identify the nature of the measurement error and design the correct set of filters.
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