Sharif Digital Repository

Control of a class of uncertain nonlinear systems which estimates unavailable state variables is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include -a strong algorithm to estimate attitude, based on discrete extended kalman filter combined with a continuous extended kalman filter and attitude nonlinear model -a robust controller based on slidingmode with perturbation estimation. The estimation result of interval kalman filtering is a sequence of interval estimates that encompasses all possible optimal attitude estimates, which the interval system may generate. The... 

In this paper we present a new landmark initialization technique for Bearing-Only Simultaneous Localization and Mapping (SLAM) algorithm. The initialization scheme is a type of the delayed constrained initialization method and is different from previous approaches. In our work, it is shown that the angle between sequential observations measured by a bearing-only sensor such as pan-tilt camera and the distance between the vehicle and landmark plays an important role in landmark localization accuracy. Considering this fact, a proper constrained function that utilizes the rotation angle of the camera is applied between multiple landmark location estimates and the best estimate is selected. The... 

In this paper an approach based on system identification is used for fault detection in a nuclear reactor. A continuous-time Extended Kalman Filter (EKF) is presented, which allows the parameters of the nonlinear system to be estimated. Also a fault tolerant control system is designed for the nuclear reactor during power changes operation. The proposed controller is an adaptive critic-based neuro-fuzzy controller. Performance of the controller in terms of transient response and robustness against failures is very good and considerable  

Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero mean and specified variance were added to simulated results by a random number generator. The generated data are transferred on-line to a developed data reconciliation software. The software performs NDDR on received data using EKF method. Comparison of data reconciliation results with simulated... 

Object-oriented modeling methodology is used for encapsulating different methods and attributes of data reconciliation (DR) in classes. Classes which are defined for DR, cover steady-state, dynamic, linear and nonlinear DR problems. Two main classes are Constraints and DR and defined for manipulating constraints and general DR problem. The remaining classes are derived from these two classes. A class namely DDRMethod is developed for encapsulating all common attributes and methods needed for any DDR method. Developed DR software and the method of performing dynamic DR are discussed in this paper. Two illustrative examples of Extended Kalman Filtering and artificial neural networks are used... 

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

This paper concerns non-linear state estimation in a batch polymerization reactor where suspension polymerization of methyl methacrylate takes place. A kinetic model proposed in the literature is selected and its validity has been verified through an experimental set-up. Based on this model monomer conversion and average molecular weights of the polymer are estimated using only one output measurement (reactor temperature). The performance of the estimator, which has the structure of an extended Kalman filter, is examined through simulation and experimental studies in the presence of different levels of parameter uncertainties. The effects of adding 'fictitious noise' and parameter state' to... 

In this paper, a high performance procedure for estimating of hydrodynamic coefficients in Autonomous Underwater Vehicles (AUV's) is proposed. In modeling of an AUV, experimental data should be verified and validated using appropriate techniques. Due to implementation complexity in calculating methods, computation of hydrodynamic parameters is challenging. This paper presents analytical approaches for estimating an AUV's hydrodynamic coefficients. Nonlinear Kalman Filter (KF) algorithms are implemented to estimate unknown augmented states (coefficients). A comparative study is conducted which shows the superior performance of Unscented Kalman Filter (UKF) in comparison with Extended Kalman... 

In this paper, LQG/LTR controller is designed for attitude control of the geostationary satellite at nominal mode. Usage actuator is the reaction wheel and control torque is determined by the LQR regulator. LQR controller signal has good performance, if all states are considered in feedback, but does not include model and sensors noises. Usage sensors are sun and earth sensors and EKF is used for estimation of noisy states. Then, LQG and LQG/LTR controllers are designed based on the estimated states, and are compared with LQR controller. The results show that robustness and performance of LQG/LTR are better than LQG and its control overshoot is smaller than LQR. The term that is provided in... 

In this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an... 

In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman... 

A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes. The introduced observer is an integral part of the control strategy of hybrid systems in general and PSA systems in particular. A reduced order model is developed based on the mechanistic model of the process. The sensors are optimally located and observability of the process is studied. The proposed observer is used to estimate the spatial profiles of various states of a two-bed, six-step PSA system used for production of pure H2 from a H2–CH4 gas mixture. The spatial profiles of the system have been estimated using... 

Most of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was... 

In non-classical micro-beams, the strain energy of the system is determined by the non-classical continuum mechanics. In this study, we consider a closed-loop control methodology for suppressing the vibration of non-classical microscale Euler-Bernoulli beams with nonlinear electrostatic actuation. The non-dimensional form of the governing nonlinear partial differential equation of the system is introduced and converted into a set of ordinary differential equations using the Galerkin projection method. In addition, we prove the observability of the system and we design a state estimation system using the extended Kalman filter algorithm. The effectiveness and performance of the proposed... 

In non-classical micro-beams, the strain energy of the system is determined by the non-classical continuum mechanics. In this study, we consider a closed-loop control methodology for suppressing the vibration of non-classical microscale Euler-Bernoulli beams with nonlinear electrostatic actuation. The non-dimensional form of the governing nonlinear partial differential equation of the system is introduced and converted into a set of ordinary differential equations using the Galerkin projection method. In addition, we prove the observability of the system and we design a state estimation system using the extended Kalman filter algorithm. The effectiveness and performance of the proposed... 

An adaptive extended Kalman filter is designed to estimate the arc length in a gas metal arc welding system. The simulation results show that the estimated variables track the true variables of the non-linear model with negligible error and are robust against parameters uncertainties. The proposed estimator also operates adequately in a highly noisy welding environment. Because of the low computational requirements and little lag produced in the process dynamic, use of the proposed estimator would be valuable in the design of a controller for the gas metal arc welding system  

In this paper, the drivetrain identification problem of a horizontal axis gear-driven wind turbine has been considered. The identification problem leads to a precise model of the drivetrain of the wind turbines which plays a key role in the production and transmission of electrical energy. This process consists of two stages: First, offline identification which needs the input–output data from the drivetrain system. These data are obtained from the FAST code. FAST (Fatigue, Aerodynamics, Structures, and Turbulence) is a valid aeroelastic code in the simulation aeroelastic field of offshore and onshore wind turbines. In region 2 (wind velocity is between the cut-in and rated velocities), the...