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Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the measured output. Recursive filters such as Kalman Filters or Extended Kalman Filters are commonly used in state estimation; however, they do not allow inclusion of constraints in their formulation. On the other hand, computational complexity of full information estimation (using all measurements) grows with iteration and becomes intractable. One way of formulating the recursive state estimation problem with constraints is the Moving Horizon Estimation (MHE) approximation. Estimates of states are calculated from the solution of a constrained optimization problem of fixed size. Detailed formulation of this strategy is studied and properties of this estimation algorithm are discussed in this work. The problem with the MHE formulation is solving an optimization problem in each iteration which is computationally intensive. State estimation with constraints can be formulated as Extended Kalman Filter (EKF) with a projection applied to estimates. The states are estimated from the measurements using standard Extended Kalman Filter (EKF) algorithm and the estimated states are projected on to a constrained set. Detailed formulation of this estimation strategy is studied and the properties associated with this algorithm are discussed. Both these state estimation strategies (MHE and EKF with projection) are tested with examples from the literature. The average estimation time and the sum of square estimation error are used to compare performance of these estimators. Results of the case studies are analyzed and trade-offs are discussed.
ContributorsJoshi, Rakesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions. In this thesis, an approach is proposed to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone, an opioid antagonist, as treatment for a chronic pain condition known as fibromyalgia. System identification techniques are employed to model the dynamics from the daily diary reports completed by participants of a blind naltrexone intervention trial. These self-reports include assessments of outcomes of interest (e.g., general pain symptoms, sleep quality) and additional external variables (disturbances) that affect these outcomes (e.g., stress, anxiety, and mood). Using prediction-error methods, a multi-input model describing the effect of drug, placebo and other disturbances on outcomes of interest is developed. This discrete time model is approximated by a continuous second order model with zero, which was found to be adequate to capture the dynamics of this intervention. Data from 40 participants in two clinical trials were analyzed and participants were classified as responders and non-responders based on the models obtained from system identification. The dynamical models can be used by a model predictive controller for automated dosage selection of naltrexone using feedback/feedforward control actions in the presence of external disturbances. The clinical requirement for categorical (i.e., discrete-valued) drug dosage levels creates a need for hybrid model predictive control (HMPC). The controller features a multiple degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed loop system. The nominal and robust performance of the proposed control scheme is examined via simulation using system identification models from a representative participant in the naltrexone intervention trial. The controller evaluation described in this thesis gives credibility to the promise and applicability of control engineering principles for optimizing adaptive interventions.
ContributorsDeśapāṇḍe, Sunīla (Author) / Rivera, Daniel E. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Membranes are a key part of pervaporation processes, which is generally a more
efficient process for selective removal of alcohol from water than distillation. It is
necessary that the membranes have high alcohol permeabilities and selectivities.
Polydimethylsiloxane (PDMS) based mixed matrix membranes (MMMs) have
demonstrated very promising results. Zeolitic imidazolate framework-71 (ZIF-71)
demonstrated promising alcohol separation abilities. In this dissertation, we present
fundamental studies on the synthesis of ZIF-71/PDMS MMMs.
Free-standing ZIF-71/ PDMS membranes with 0, 5, 25 and 40 wt % ZIF-71
loadings were prepared and the pervaporation separation for ethanol and 1-butanol from
water was measured. ZIF-71/PDMS MMMs were formed through addition cure and
condensation cure methods. Addition cure method was not compatible with ZIF-71
resulting in membranes with poor mechanical properties, while the condensation cure
method resulted in membranes with good mechanical properties. The 40 wt % ZIF-71
loading PDMS nanocomposite membranes achieved a maximum ethanol/water selectivity
of 0.81 ± 0.04 selectivity and maximum 1-butnaol/water selectivity of 5.64 ± 0.15.
The effects of synthesis time, temperature, and reactant ratio on ZIF-71 particle
size and the effect of particle size on membrane performance were studied. Temperature
had the greatest effect on ZIF-71 particle size as the synthesis temperature varied from -
20 to 35 ºC. The ZIF-71 synthesized had particle diameters ranging from 150 nm to 1
μm. ZIF-71 particle size is critical in ZIF-71/PDMS composite membrane performance
for alcohol removal from water through pervaporation. The membranes made with
micron sized ZIF-71 particles showed higher alcohol/water selectivity than those with
smaller particles. Both alcohol and water permeability increased when larger sized ZIF-
71 particles were incorporated.
ZIF-71 particles were modified with four ligands through solvent assisted linker
exchange (SALE) method: benzimidazole (BIM), 5-methylbenzimidazole (MBIM), 5,6-
dimethylbenzimidazole (DMBIM) and 4-Phenylimidazole (PI). The morphology of ZIF-
71 were maintained after the modification. ZIF-71/PDMS composite membranes with 25
wt% loading modified ZIF-71 particles were made for alcohol/water separation. Better
particle dispersion in PDMS polymer matrix was observed with the ligand modified ZIFs.
For both ethanol/water and 1-butanol/water separations, the alcohol permeability and
alcohol/water selectivity were lowered after the ZIF-71 ligand exchange reaction.
efficient process for selective removal of alcohol from water than distillation. It is
necessary that the membranes have high alcohol permeabilities and selectivities.
Polydimethylsiloxane (PDMS) based mixed matrix membranes (MMMs) have
demonstrated very promising results. Zeolitic imidazolate framework-71 (ZIF-71)
demonstrated promising alcohol separation abilities. In this dissertation, we present
fundamental studies on the synthesis of ZIF-71/PDMS MMMs.
Free-standing ZIF-71/ PDMS membranes with 0, 5, 25 and 40 wt % ZIF-71
loadings were prepared and the pervaporation separation for ethanol and 1-butanol from
water was measured. ZIF-71/PDMS MMMs were formed through addition cure and
condensation cure methods. Addition cure method was not compatible with ZIF-71
resulting in membranes with poor mechanical properties, while the condensation cure
method resulted in membranes with good mechanical properties. The 40 wt % ZIF-71
loading PDMS nanocomposite membranes achieved a maximum ethanol/water selectivity
of 0.81 ± 0.04 selectivity and maximum 1-butnaol/water selectivity of 5.64 ± 0.15.
The effects of synthesis time, temperature, and reactant ratio on ZIF-71 particle
size and the effect of particle size on membrane performance were studied. Temperature
had the greatest effect on ZIF-71 particle size as the synthesis temperature varied from -
20 to 35 ºC. The ZIF-71 synthesized had particle diameters ranging from 150 nm to 1
μm. ZIF-71 particle size is critical in ZIF-71/PDMS composite membrane performance
for alcohol removal from water through pervaporation. The membranes made with
micron sized ZIF-71 particles showed higher alcohol/water selectivity than those with
smaller particles. Both alcohol and water permeability increased when larger sized ZIF-
71 particles were incorporated.
ZIF-71 particles were modified with four ligands through solvent assisted linker
exchange (SALE) method: benzimidazole (BIM), 5-methylbenzimidazole (MBIM), 5,6-
dimethylbenzimidazole (DMBIM) and 4-Phenylimidazole (PI). The morphology of ZIF-
71 were maintained after the modification. ZIF-71/PDMS composite membranes with 25
wt% loading modified ZIF-71 particles were made for alcohol/water separation. Better
particle dispersion in PDMS polymer matrix was observed with the ligand modified ZIFs.
For both ethanol/water and 1-butanol/water separations, the alcohol permeability and
alcohol/water selectivity were lowered after the ZIF-71 ligand exchange reaction.
ContributorsYin, Huidan (Author) / Lind, Mary Laura (Thesis advisor) / Mu, Bin (Committee member) / Nielsen, David (Committee member) / Seo, Don (Committee member) / Lin, Jerry (Committee member) / Arizona State University (Publisher)
Created2017
Description
Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
ContributorsLee, Jongmin (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems.
Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation.
A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance.
Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.
Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation.
A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance.
Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.
ContributorsJoshi, Rakesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Torres, Cesar (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Microbial fuel cells (MFCs) promote the sustainable conversion of organic matter in black water to electrical current, enabling the production of hydrogen peroxide (H2O2) while making waste water treatment energy neutral or positive. H2O2 is useful in remote locations such as U.S. military forward operating bases (FOBs) for on-site tertiary water treatment or as a medical disinfectant, among many other uses. Various carbon-based catalysts and binders for use at the cathode of a an MFC for H2O2 production are explored using linear sweep voltammetry (LSV) and rotating ring-disk electrode (RRDE) techniques. The oxygen reduction reaction (ORR) at the cathode has slow kinetics at conditions present in the MFC, making it important to find a catalyst type and loading which promote a 2e- (rather than 4e-) reaction to maximize H2O2 formation. Using LSV methods, I compared the cathodic overpotentials associated with graphite and Vulcan carbon catalysts as well as Nafion and AS-4 binders. Vulcan carbon catalyst with Nafion binder produced the lowest overpotentials of any binder/catalyst combinations. Additionally, I determined that pH control may be required at the cathode due to large potential losses caused by hydroxide (OH-) concentration gradients. Furthermore, RRDE tests indicate that Vulcan carbon catalyst with a Nafion binder has a higher H2O2 production efficiency at lower catalyst loadings, but the trade-off is a greater potential loss due to higher activation energy. Therefore, an intermediate catalyst loading of 0.5 mg/cm2 Vulcan carbon with Nafion binder is recommended for the final MFC design. The chosen catalyst, binder, and loading will maximize H2O2 production, optimize MFC performance, and minimize the need for additional energy input into the system.
ContributorsStadie, Mikaela Johanna (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
In our modern world the source of for many chemicals is to acquire and refine oil. This process is becoming an expensive to the environment and to human health. Alternative processes for acquiring the final product have been developed but still need work. One product that is valuable is butanol. The normal process for butanol production is very intensive but there is a method to produce butanol from bacteria. This process is better because it is more environmentally safe than using oil. One problem however is that when the bacteria produce too much butanol it reaches the toxicity limit and stops the production of butanol. In order to keep butanol from reaching the toxicity limit an adsorbent is used to remove the butanol without harming the bacteria. The adsorbent is a mesoporous carbon powder that allows the butanol to be adsorbed on it. This thesis explores different designs for a magnetic separation process to extract the carbon powder from the culture.
ContributorsChabra, Rohin (Author) / Nielsen, David (Thesis director) / Torres, Cesar (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
Microbial fuel cells (MFCs) facilitate the conversion of organic matter to electrical current to make the total energy in black water treatment neutral or positive and produce hydrogen peroxide to assist the reuse of gray water. This research focuses on wastewater treatment at the U.S. military forward operating bases (FOBs). FOBs experience significant challenges with their wastewater treatment due to their isolation and dangers in transporting waste water and fresh water to and from the bases. Even though it is theoretically favorable to produce power in a MFC while treating black water, producing H2O2 is more useful and practical because it is a powerful cleaning agent that can reduce odor, disinfect, and aid in the treatment of gray water. Various acid forms of buffers were tested in the anode and cathode chamber to determine if the pH would lower in the cathode chamber while maintaining H2O2 efficiency, as well as to determine ion diffusion from the anode to the cathode via the membrane. For the catholyte experiments, phosphate and bicarbonate were tested as buffers while sodium chloride was the control. These experiments determined that the two buffers did not lower the pH. It was seen that the phosphate buffer reduced the H2O2 efficiency significantly while still staying at a high pH, while the bicarbonate buffer had the same efficiency as the NaCl control. For the anolyte experiments, it was shown that there was no diffusion of the buffers or MFC media across the membrane that would cause a decrease in the H2O2 production efficiency.
ContributorsThompson, Julia (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05