Application of Optimal Filtering
Technique for Analytical Streamflow Forecasting, (pdf 9 kB), Vinay Kantamaneni
The main aim of this research is to develop a streamflow forecasting model with the help of the Sacramento Catchment Model and apply optimal filtering techniques to estimate the states. There have been many models in the past for flow forecasting. But most of them could not effectively give the result for a long period of time where the rainfall is infrequent. The Sacramento model is streamflow forecasting model which was effectively useful for the region where the antecedent approach was deficient. The purpose of this research work is to develop and simulate the Sacramento Model in Matlab and then use optimal filtering techniques in order to predict hydrological variables. In general hydrology is the study of the waters of the earth, especially with relation to the effects of precipitation and evaporation upon the occurrence and character of water in streams and lakes, and water on or below the land surface. The Sacramento Catchment Model is the “Generalized Streamflow Forecasting Model” which was developed by personnel at the California-Nevada River Forecast Center. It was developed by Burnash et al in 1973 forecast the river flow in the California-Nevada region where the antecedent approach was deficient.
Health Parameter Estimation
of turbofan aircarft engine, (pdf 466 KB ), Srikiran Kosanam
Aircraft health monitoring has been a challenging task for over decades. In turbofan jet engines all the parameters which describe the health of the engine cannot be measured explicitly. One possible solution to this problem is Kalman filter. The traditional Kalman filter is optimal as long as the modeling of the plant is accurate. The turbofan jet engine being highly non-linear makes the task difficult. This thesis shows a way of linearizing the jet engine model so that theoretically proven estimation techniques can be applied to this problem. This thesis presents the application of Kalman filter to health parameter monitoring of the gas turbine engine. It is shown that the standard Kalman filter will not be robust enough if there are uncertainties in the modeling of the plant. A new filter is developed in this thesis which addresses the uncertainties in the process noise and measurement noise covariances without assuming any bounds on them. A hybrid gradient descent algorithm is proposed to tune the new filter gain. This filter is then implemented for the health parameter estimation. The results show significant decrease in the estimation error covariance. It is shown in the conclusions that advanced search algorithms like Genetic Algorithms proves to be superior to hybrid gradient descent algorithm in searching for better minima.
health estimation of a turbofan engine, (pdf 1MB), Bharath Reddy Endurthi
Investigate health parameter deterioration of a turbofan aircraft engine over a period of time. In order to do this we need to measure the health parameters, but unfortunately it is quite impossible to measure all the health parameters. So the concept of estimation is brought into consideration. The Kalman filter has been shown to be the best estimator as far as linear systems are concerned. So the nonlinear engine model is linearized with respect to three different parameters using three different methods. This gives us 27 different linear models for one nonlinear engine model. The different linearization methods that will be discussed are the Matlab method, the perturbation method and and the steady state error reduction method. Kalman filtering results are investigated for all 27 different linear models.
Sensorless control of stepper motor using Kalman filtering, (pdf 2.88MB), Chirayu Shah
Despite model improvements and different control algorithms, much work remains to be done to attain maximum motor performance. This work attempts to achieve control and velocity tracking for a step motor using optimization techniques. The resulting system displays practical stabilization for velocity tracking of a voltage-fed permanent-magnet stepper motor. The control design is an output-feedback design that utilizes stator current and rotor position measurements. The goal of this work is to design a controller that is robust to load torques, cogging forces, and other disturbances satisfying certain bounds by estimating the speed and position of the motor using an extended Kalman filter. The controller and the estimator are implemented in a Microchip PIC16F877 microcontroller using embedded technology, and speed control is achieved using the estimated motor state. The PIC16F877 is interfaced to a PC through USART communication to send the real time data to the PC for analysis and comparison with Matlab results.