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You searched for subject:(Feedback particle filter). Showing records 1 – 3 of 3 total matches.

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University of Illinois – Urbana-Champaign

1. Medarametla, Krishna Kalyan. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.

Degree: MS, 0133, 2014, University of Illinois – Urbana-Champaign

In a recent work it has been shown that importance sampling can be avoided in particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control and mean-field game formalisms. The resulting algorithm is referred to as feedback particle filter (FPF). The purpose of this thesis is to provide a comparative study of the feedback particle filter (FPF) with the extended Kalman filter (EKF) for a scalar filtering problem which has linear signal dynamics and nonlinear observation dynamics. Different parameters of the signal model and observation model will be varied and performance of the two filtering techniques FPF, EKF will be compared. Advisors/Committee Members: Mehta, Prashant G. (advisor).

Subjects/Keywords: Extended Kalman filter; Feedback particle filter; Comparison; Nonlinear filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Medarametla, K. K. (2014). Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed March 24, 2019. http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Web. 24 Mar 2019.

Vancouver:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Ghiotto, Shane. Comparison of nonlinear filtering techniques.

Degree: MS, 0133, 2014, University of Illinois – Urbana-Champaign

In a recent work it is shown that importance sampling can be avoided in the particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control formalisms. The resulting algorithm is referred to as feedback particle filter. The purpose of this thesis is to provide a comparative study of the feedback particle filter (FPF). Two types of comparisons are discussed: i) with the extended Kalman filter, and ii) with the conventional resampling-based particle filters. The comparison with Kalman filter is used to highlight the feedback structure of the FPF. Also computational cost estimates are discussed, in terms of number of op- erations relative to EKF. Comparison with the conventional particle filtering ap- proaches is based on a numerical example taken from the survey article on the topic of nonlinear filtering. Comparisons are provided for both computational cost and accuracy. Advisors/Committee Members: Mehta, Prashant G. (advisor).

Subjects/Keywords: Filtering; state estimation; particle filtering; Kalman filter; feedback particle filter

…approaches, such as the feedback particle filter (FPF) in [16, 14], seek to… …with the feedback particle filter. The remainder of the thesis is organized as follows… …chapter 2 provides background on the extended Kalman filter and feedback particle filter… …the feedback particle filter. Chapter 4 then provides a series of benchmark 2 problems… …introduction to the algorithms used for the feedback particle filter and the extended Kalman filter… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ghiotto, S. (2014). Comparison of nonlinear filtering techniques. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/49437

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ghiotto, Shane. “Comparison of nonlinear filtering techniques.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed March 24, 2019. http://hdl.handle.net/2142/49437.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ghiotto, Shane. “Comparison of nonlinear filtering techniques.” 2014. Web. 24 Mar 2019.

Vancouver:

Ghiotto S. Comparison of nonlinear filtering techniques. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2142/49437.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghiotto S. Comparison of nonlinear filtering techniques. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/49437

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Lund

3. Turesson, Gabriel. Model-Based Optimization of Combustion-Engine Control.

Degree: 2018, University of Lund

The work presented in this thesis is motivated by the need to reliably operate a compression-ignition engine in a partially premixed combustion (PPC) mode. Partially premixed combustion is a low temperature combustion concept, where the ignition delay is prolonged to enhance fuel-air mixing in the combustion chamber before the start of combustion. A premixed combustion process, in combination with high levels of exhaust-gas recirculation (EGR), gives low combustion temperatures, which decrease NOx and soot formation. Lowered combustion temperatures also reduce heat-transfer losses which increase the thermodynamic engine efficiency. The ignition delay is, however, determined by chemical reactions rates, which are sensitive to temperature, gas-mixture composition, fuel properties and fuel-injection timing. This sensitivity makes PPC more challenging to operate compared to conventional diesel combustion. Challenges related to PPC include an increased sensitivity to operating conditions, decreased combustion-timing controllability, high pressure-rise rates, and low combustion efficiency at low engine loads. These challenges put high demands on the engine control system that needs to be able to adjust fuel-injection timings and durations to compensate for the combustion sensitivity. Therefore, this thesis investigates closed-loop combustion control for reliable PPC operation. The feedback loop from pressure-sensor measurement to fuel-injection actuation is studied in particular. A common theme for the controllers presented is the use of models in the controller design. Either to evaluate controller performance in simulation, or to optimize engine performance online. The principle of model predictive control is used for its ability to incorporate modeled system behavior in the controller design, and to control multi-variable systems with input and output constraints.The problem of tuning robust and noise insensitive combustion-timing controllers, and its dependence on fuel reactivity is studied in simulation. Simulation results reveal a nonlinear relation between start of injection and combustion timing that depends on both load and fuel reactivity. Optimization is used to find robust and noise-insensitive controller gains. Guidelines for combustion-timing controller tuning are also presented. Low-order autoignition models are evaluated and compared for the purpose of model-based controller design. The comparison shows that a simple autoignition model is sufficient for control of the ignition delay when the cylinder-charge properties are varied. This model is then used by a model predictive controller to simultaneously control ignition delay and combustion timing in transient engine operation, using both gas-exchange and fuel-injection actuation.The effects of pilot injection on the combustion processes are characterized experimentally. Experimental results show that a pilot injection can decrease the main-injection ignition delay and maintain the pressure-rise rate at an acceptable level. This is utilized by a presented…

Subjects/Keywords: Teknik och teknologier; Model Predictive Control (MPC), Partially Premixed Combustion, Pressure Sensor Feedback, Model Based Control, Particle Filter, Multiple Fuel Injections, Gasoline Compression Ignition; Model Predictive Control (MPC); Partially Premixed Combustion (PPC); Low Temperature Combustion; Model Based Control; Multiple Fuel Injections; Pressure Sensor Feedback; Particle Filter; Gasoline Compression Ignition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Turesson, G. (2018). Model-Based Optimization of Combustion-Engine Control. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/aad1e914-8d86-4dc4-a4f8-f3083d54bfd8 ; http://portal.research.lu.se/ws/files/42503822/thesis.pdf

Chicago Manual of Style (16th Edition):

Turesson, Gabriel. “Model-Based Optimization of Combustion-Engine Control.” 2018. Doctoral Dissertation, University of Lund. Accessed March 24, 2019. http://lup.lub.lu.se/record/aad1e914-8d86-4dc4-a4f8-f3083d54bfd8 ; http://portal.research.lu.se/ws/files/42503822/thesis.pdf.

MLA Handbook (7th Edition):

Turesson, Gabriel. “Model-Based Optimization of Combustion-Engine Control.” 2018. Web. 24 Mar 2019.

Vancouver:

Turesson G. Model-Based Optimization of Combustion-Engine Control. [Internet] [Doctoral dissertation]. University of Lund; 2018. [cited 2019 Mar 24]. Available from: http://lup.lub.lu.se/record/aad1e914-8d86-4dc4-a4f8-f3083d54bfd8 ; http://portal.research.lu.se/ws/files/42503822/thesis.pdf.

Council of Science Editors:

Turesson G. Model-Based Optimization of Combustion-Engine Control. [Doctoral Dissertation]. University of Lund; 2018. Available from: http://lup.lub.lu.se/record/aad1e914-8d86-4dc4-a4f8-f3083d54bfd8 ; http://portal.research.lu.se/ws/files/42503822/thesis.pdf

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