Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"state and parameter estimation"”.
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Combined state and parameter estimation for on-line applications,
… Institute of Technology, Dept. of Aeronautics and Astronautics, 1972.
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Application of Digital Filtering Methods to State and Parameter Estimation in Process Plant
The application of on-line state variable and parameter estimation for chemical processes, with particular reference to a pilot plant scale double effect evaporator, has been investigated. The investigation has shown the requirement of some adaptive modification to the recursive Kalman Filter to be …
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Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering
… Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to extend the work of Pitt and Shephard (1999) and Liu and Chen (1998) to adapt particle filtering to latent state and parameter estimation in stochastic volatility/jump models. This dissertation will review …
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Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods
… coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system. Facing this problem, traditional methods, such as the Monte Carlo method and the Perturbation method, are either too time consuming or suffering …
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A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control
The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state …
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State and parameter estimation of power systems using phasor measurement units as bilinear system model
In the wake of past various blackouts, management and control of power system is going through essential transitions. To transform the modern grid into smart grid, various actions related to data acquisition and processing, security monitoring and control and energy/economy decisions have to be …
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Pseudo-linear indentification and its application to adaptive control
… as an explicitly linear approach to the joint state and parameter estimation problem. The convergence properties of the algorithm in the stochastic case are investigated through simulation and comparisons with popular methods are made. The method is then extended to allow the tracking of …
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Parameter estimation in reconstructing temperature fields during hyperthermia.
In this dissertation, a state and parameter estimation algorithm is implemented and modified to predict the blood perfusions and thus the complete steady-state temperature fields based on input from a limited number of temperature measurements taken during simulated hyperthermia treatments. Several …
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Observer-Based Simultaneous States and Parameters Estimation Method with Application to System Heath Monitoring
Joint state and parameter estimation is paramount in many engineering and scientific fields, as it involves determining the internal states of a system and the estimation of its time-varying / unknown parameters simultaneously. This twin estimation aspect is crucial for real-time system monitoring, …
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Nonlinear Adaptive Estimation Andits Application To Synchronization Of Lorenz System
Synchronization and estimation of unknown constant parameters for Lorenz-type transmitter are studied under the assumption that one of the three state variables is not transmitted and that transmitter parameters are not known apriori. An adaptive algorithm is proposed to estimate both the state and …
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Robust Adaptive Rigid Body State and Mass Property Estimation Via Unscented Kalman Filter on TSE(3) with Process Noise Estimation
<p>Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a …
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Advanced Braking Systems for Heavy Vehicles
… the use of advanced emergency braking hardware and wheel slip control algorithms on pneumatically braked heavy goods vehicles. Anti-lock braking systems (ABS) on commercial heavy vehicles use inefficient control approaches that work on cycles of exceeding the limits of tyre-road adhesion. Long …
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Distributed Target Tracking and Synchronization in Wireless Sensor Networks
… challenges in scalable information processing and network maintenance. This dissertation focuses on statistical methods for distributed information fusion and sensor synchronization for target tracking in wireless sensor networks.</p> <p>We perform target tracking using particle filtering. For …
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Completely Recursive Least Squares and Its Applications
… squares (RLS) approach is of a recursive form and free of matrix inversion, and has excellent performance regarding computation and memory in solving the classic least-squares (LS) problem. It is important to generalize RLS for generalized LS (GLS) problem. It is also of value to develop an …
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Development of an Intelligent Tire Based Tire - Vehicle State Estimator for Application to Global Chassis Control
The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, under the circumstances of sudden changes to the road conditions, the driver`s ability to maintain control of the vehicle maybe at risk. In many cases, this requires …
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Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling
… models are used for prediction in many domains, and are useful to mitigate many of the grand challenges being faced by humanity, such as climate change, food security, and sustainability. However, because of computational costs, complexity of real-world phenomena, and limited understanding of the …