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Full-Text Articles in Engineering
State Estimation Based On Nested Particle Filters, Swathi Srinivasan
State Estimation Based On Nested Particle Filters, Swathi Srinivasan
ETD Archive
In reality many processes are nonlinear and in order to have a knowledge about the true process conditions, it is important to make decisions based on the state of the system. Process measurements such as pressure, temperature, and pH, are available at time instances and this information is necessary in order to obtain the state of the system. Filtering is a state estimation technique by which the estimate is obtained at a time instant, given the process measurements at their respective time instances. Several lters have been developed so far for the estimation of the states of the system. Kalman …
Sensing And Control Of Mems Accelerometers Using Kalman Filter, Kai Zhang
Sensing And Control Of Mems Accelerometers Using Kalman Filter, Kai Zhang
ETD Archive
Surface micromachined low-capacitance MEMS capacitive accelerometers which integrated CMOS readout circuit generally have a noise above 0.02g. Force-to-rebalance feedback control that is commonly used in MEMS accelerometers can improve the performances of accelerometers such as increasing their stability, bandwidth and dynamic range. However, the controller also increases the noise floor. There are two major sources of the noise in MEMS accelerometer. They are electronic noise from the CMOS readout circuit and thermal-mechanical Brownian noise caused by damping. Kalman filter is an effective solution to the problem of reducing the effects of the noises through estimating and canceling the states contaminated …
Biogeography-Based Optimization: Synergies With Evolutionary Strategies, Immigration Refusal, And Kalman Filters, Dawei Du
ETD Archive
Biogeography-based optimization (BBO) is a recently developed heuristic algorithm which has shown impressive performance on many well known benchmarks. The aim of this thesis is to modify BBO in different ways. First, in order to improve BBO, this thesis incorporates distinctive techniques from other successful heuristic algorithms into BBO. The techniques from evolutionary strategy (ES) are used for BBO modification. Second, the traveling salesman problem (TSP) is a widely used benchmark in heuristic algorithms, and it is considered as a standard benchmark in heuristic computations. Therefore the main task in this part of the thesis is to modify BBO to …
Nonlinear State Estimation In Polymer Electrolyte Membrane Fuel Cells, Uma Tumuluri
Nonlinear State Estimation In Polymer Electrolyte Membrane Fuel Cells, Uma Tumuluri
ETD Archive
Research on alternative and renewable energy sources which are amicable to the environment has gained momentum because of the growing concern about the tremendous increase in the concentration of toxic and green house gases and scarcity of the fossil fuels. Among the available renewable sources, fuel cell technology has received a high research attention due to their high efficiency and superior reliability. Among the various fuel cells available, Polymer electrolyte membrane fuel cell is promising source for both stationary and mobile applications because of its high efficiency and low operating temperatures. The performance of the fuel cell depends on the …
Kalman Filtering With Inequality Constraints For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon
Kalman Filtering With Inequality Constraints For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon
Electrical and Computer Engineering Faculty Publications
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. Thus, two analytical methods to incorporate state-variable inequality constraints into the Kalman filter are now derived. The first method is a general technique that uses hard constraints to enforce inequalities on the state-variable estimates. The resultant filter is a combination …
H-Infinity Estimation For Fuzzy Membership Function Optimization, Daniel J. Simon
H-Infinity Estimation For Fuzzy Membership Function Optimization, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. …
Kalman Filtering With Uncertain Noise Covariances, Srikiran Kosanam, Daniel J. Simon
Kalman Filtering With Uncertain Noise Covariances, Srikiran Kosanam, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine.
Sum Normal Optimization Of Fuzzy Membership Functions, Daniel J. Simon
Sum Normal Optimization Of Fuzzy Membership Functions, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This …
Kalman Filtering With State Equality Constraints, Daniel J. Simon, Tien Li Chia
Kalman Filtering With State Equality Constraints, Daniel J. Simon, Tien Li Chia
Electrical and Computer Engineering Faculty Publications
Kalman filters are commonly used to estimate the states of a dynamic system. However, in the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically. For instance, constraints on state values (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. A rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed. The constraints may be time varying. At each time step the unconstrained Kalman filter solution is projected onto the state …