Tracking Uncertainty Research Articles

Initial needle tracking with the first standalone combined infrared camera - CT system for brachytherapy-analysis of tracking accuracy and uncertainties.

Aprototype infrared camera - cone-beam computed tomography (CBCT) system for tracking in brachytherapy has recently been developed. We evaluated for the first time the corresponding tracking accuracy and uncertainties, and implemented atracking-based prediction of needles on CBCT scans. Amarker tool rigidly attached to needles was 3D printed. The precision and accuracy of tool tracking was then evaluated for both static and dynamic scenarios. Euclidean distances between the tracked and CBCT-derived markers were assessed as well. To implement needle tracking, ground truth models of the tool attached to 200 mm and 160 mm needles were matched to the tracked positions in order to project the needles into CBCT scans. Deviations between projected and actual needle tips were measured. Finally, we put our results into perspective with simulations of the system's tracking uncertainties. For the stationary scenario and dynamic movements, we achieved tool-tracking precision and accuracy of 0.04 ± 0.06 mm and 0.16 ± 0.18 mm, respectively. The tracked marker positions differed by 0.52 ± 0.18 mm from the positions determined via CBCT. In addition, the predicted needle tips in air deviated from the actual tip positions by only 1.62 ± 0.68 mm (200 mm needle) and 1.49 ± 0.62 mm (160 mm needle). The simulated tracking uncertainties resulted in tip variations of 1.58 ± 0.91 mm and 1.31 ± 0.69 mm for the 200 mm and 160 mm needles, respectively. With the innovative system it was possible to achieve ahigh tracking and prediction accuracy of marker tool and needles. The system shows high potential for applicator tracking in brachytherapy.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy—a proof-of-principle study

Objective. High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom. Approach. Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of the in vivo dosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification. Main results. The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time. Significance. Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Tracking uncertainty in germline genetic testing for hereditary cancer syndromes: Sources, attributes, and resolution of variants of uncertain significance in over 1 million individuals.

10513 Background: Genetic testing for hereditary cancer syndromes has diagnostic, prognostic and therapeutic implications; however, variant(s) of uncertain significance (VUS) are not clinically actionable. As such, VUS are a challenge to the entire medical community. Additionally, individuals from underrepresented race, ethnicity, and ancestry (REA) groups are disproportionately impacted by VUS. This study reports on the prevalence of VUS in patients referred for genetic testing for hereditary cancer syndromes and the results of reclassification. Methods: Patients were referred for diagnostic multigene panel testing for hereditary cancer from September 2014 to September 2022. Variants were classified as benign (B), likely benign (LB), VUS, likely pathogenic (LP), or pathogenic (P) using Sherloc, a validated system based on guidelines from ACMG/AMP. Both the number of unique VUS (uVUS) and the number of times they were observed in individuals (oVUS) were counted. All-time uVUS were separated as being reclassified and non-reclassified, and the relative contribution of evidence types used for reclassification was analyzed. Results: During this 8-year period, 1,122,444 unrelated individuals were tested for hereditary cancer with a mean number of 53 genes tested per individual. Results showed a mean of 0.45 oVUS per individual; 30.6% of individuals without a P/LP variant had at least 1 oVUS. The rate of oVUS normalized to the number of genes tested was highest in French Canadian and lowest in Ashkenazi Jewish individuals, as the mean number of genes sequenced to observe one VUS was 39.8 and 59.4, respectively. White individuals had a lower oVUS rate of 31.4% than Sephardic Jewish (53.8%), Asian (48.3%), Black (40.5%), Hispanic (37.6%), and additional underrepresented REA groups. In total, 7,542 (7.3%) of 103,767 uVUS were reclassified, affecting 88,877 individuals (7.9%); 5,864 (77.8%) of the reclassified uVUS were downgraded to B/LB, and 1,678 (22.2%) were upgraded to LP/P. Evidence from clinical observation contributed the most for upgrading uVUS (16.0-fold compared to non-reclassified uVUS), while evidence from experimental studies contributed the most to downgrading uVUS (23.9-fold). Conclusions: Experimental studies and clinical evidence were the most impactful types of evidence for reclassifications. This highlights the partnership among researchers, clinicians, and laboratories to resolve VUS. Further representation of diverse REA groups in genomic databases is needed to address the disparities in VUS rates, and additional research is needed into methods that could expedite the resolution of VUS so that patients can make more informed decisions.

Shell Reworking Impacts on Climate Variability Reconstructions Using Individual Foraminiferal Analyses

AbstractParticle mixing by benthic fauna beneath the sediment‐water interface (or bioturbation) fundamentally challenges the proxy based retrieval of past climatic conditions from deep‐sea sediment cores. Previous efforts targeted the impacts of bioturbation on the nature of paleoceanographic changes gleaned from the proxy record, whereas impacts on seasonal and/or interannual variability reconstructions have received less attention. We present TurbIFA (Tracking uncertainty of reworking & bioturbation on IFA), a software that adapts and combines existing algorithms to quantitatively estimate the impact of sediment reworking and other uncertainties and assess significance of ocean and climate variability reconstructions based on individual foraminiferal analyses (IFA). Building upon previous idealized investigations of bioturbation using hydroclimate‐sediment simulations, TurbIFA advances the IFA proxy system modeling such that users may directly assess the sensitivity of their data to various local parameters related to shell reworking across the global ocean. Using the output of state‐of‐the‐art coupled atmosphere‐ocean general circulation models, TurbIFA simulates planktic foraminiferal δ18O or Mg/Ca‐temperature signal carriers and evaluates uncertainties in the sample size, analytical protocols along with as those arising from bioturbation. Application of TurbIFA to synthetic and existing data sets indicates that the significance of IFA‐based reconstructions can be assessed once the impacts of sediment accumulation rates, sediment mixed layer depths, length of time integrated by the chosen IFA sampling interval, and changes in the amplitude of climate variability (i.e., the targeted environmental signal) are comprehensively evaluated. We contend that TurbIFA can aid quantitative assessments of past seasonal and interannual variability gleaned from the paleoceanographic record.

18 Tracking Uncertainty in Germline Genetic Testing for Inborn Errors of Immunity: Sources, Attributes, and Resolution of Variants of Uncertain Significance in Over 44,000 Individuals

18 Tracking Uncertainty in Germline Genetic Testing for Inborn Errors of Immunity: Sources, Attributes, and Resolution of Variants of Uncertain Significance in Over 44,000 Individuals

Neural network compensator-based robust iterative learning control scheme for mobile robots nonlinear systems with disturbances and uncertain parameters

Aiming at the problem of trajectory tracking control for mobile robot nonlinear systems with non-repetitive uncertain parameters, we propose a novel neural network compensator-based robust iterative learning control (NNRILC) scheme to achieve excellent tracking performance and uncertainty compensation. The NNRILC scheme consists of a parallel structure that includes a robust iterative learning control (RILC) term and a neural network (NN) compensator. The RILC term is used to ensure robust control performance and promise closed-loop stability. The compensator based on the neural network is employed to handle the uncertainties resulted from the nonlinear dynamics as well as the suppressed disturbances in the mobile robot nonlinear systems. Additionally, the fourth-order Runge-Kutta algorithm is employed to solve the state differential equation of the mobile robot. Consequently, The H∞ robust technique is used to construct an iterative learning control update rule to reduce the impact of disturbances. Meanwhile, the RILC scheme is designed to optimize the neural network initial parameters and weights of the NN compensator at each trial. Then the convergence of the proposed NNRILC scheme is analyzed, and the results are incorporated into the control update for the next process trial. Finally, the effectiveness of the proposed method is verified by mobile robot simulation.

Adaptive digital twin for pressure swing adsorption systems: Integrating a novel feedback tracking system, online learning and uncertainty assessment for enhanced performance

This paper presents a novel approach to digitizing and modeling pressure swing adsorption (PSA) processes using an uncertainty-aware digital twin. PSA modeling presents unique challenges due to its complex and cyclic behavior, which lacks a steady state. By contributing to the literature on periodic systems, we provide valuable insights into the potential applications of artificial intelligence and digital twins beyond the field of cyclic processes. Our proposed methodology can enhance the understanding and optimization of complex systems across various industries and applications. The proposed digital twin is uncertainty-aware and reliable, continuously updating itself through online learning and utilizing a novel feedback tracker to accurately represent the PSA system. This robust and adaptable methodology supports optimal PSA system operation and facilitates informed decision-making for enhanced process operation. The results demonstrate that the proposed approach yields a reliable digital twin for the PSA unit, capable of tracking the process’s complex dynamics and adapting to changes, including adsorbent degradation, which is a significant challenge in PSA operations. Overall, this work highlights the potential of advanced technologies, such as digital twins and artificial intelligence, to improve performance and efficiency in the field of process engineering. This work contributes to the ongoing efforts to optimize industrial processes and support sustainable development by providing a reliable and adaptable methodology for digitizing PSA processes.

A quality evaluation method for intelligent traffic management system based on business data flow

Intelligent Traffic Management System (ITMS) is a complex and intelligent cyber-physical system (CPS) with multi-subsystem interaction, which plays a significant role in traffic safety. However, the quality evaluation requirements of ITMS, particularly its running quality, cannot be satisfied by the current quality evaluation metrics. Moreover, the present ITMS evaluation techniques are arbitrary. The effectiveness of road traffic is impacted because ITMS quality cannot be adequately assured. To fill this gap, this paper proposes a quality evaluation (QE) methodology based on the ITMS business data flow. First, the ITMS QE dimension extraction process was introduced to describe the ITMS architecture and activities; then the new evaluation indexes including intelligence, complexity and interactivity were proposed and an ITMS QE model was established; further through the measurement of metrics elements, the quality score of the indicators were calculated; finally a prototype tool was developed to verify the efficacy and practicability of the method. The results showed that the proposed method has the advantages of accurate problem tracking and decrease decision-making uncertainty. This is applicable to the ITMS QE in various operational scenarios.

Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields

Accurate prediction of the trajectories of material drifting on the ocean surface is critical for risk assessment and responses to environmental emergencies. Prediction of these trajectories is subject to uncertainty arising from a number of sources, with a primary source being uncertainty in the modelled ocean surface currents and winds used as input to the trajectory model. This article presents a fuzzy number-based algorithm for propagating uncertainty through a particle tracking scheme in a time- and space-varying velocity field. The performance of the algorithm was tested by applying it to idealized, analytical velocity fields and scoring the results against the analytical solution. Both epistemic and aleatoric uncertainty were considered and combined using a fractional Brownian motion model for temporal autocorrelation of the uncertainty. In the evaluation of the algorithm, sensitivity was quantified with respect to parameters such as timestep size, resolution of the forcing velocity field, spatial and temporal gradients in the forcing, and resolution of the applied uncertainty. Parameter values optimizing uncertainty representation and computational cost were identified. The applied uncertainty was found to evolve in agreement with classical relative dispersion relationships.

TelecomTM

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively installed telecommunication fiber-optic cables at the roadside, our system using redundant dark fibers enables to monitor traffic at low cost with low maintenance. Many existing traffic monitoring approaches use cameras, piezoelectric sensors, and smartphones, but they are limited due to privacy concerns and/or deployment requirements. Previous studies attempted to use telecommunication cables for traffic monitoring, but they were only exploratory and limited to simple tasks at a coarse granularity, e.g., vehicle detection, due to their hardware constraints and real-world challenges. In particular, those challenges are 1) unknown and heterogeneous properties of virtual sensors and 2) large and complex noise conditions. To this end, our TelecomTM system first characterizes the geographic location and analyzes the signal pattern of each virtual sensor through driving tests. We then develop a spatial-domain Bayesian filtering and smoothing algorithm to detect, track, and characterize each vehicle. Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking. In our real-world evaluation on a two-way traffic road with 1120 virtual sensors, TelecomTM achieved 90.18% vehicle detection accuracy, 27x and 5x error reduction for vehicle position and speed tracking compared to a baseline method, and ±3.92% and ±11.98% percent error for vehicle wheelbase and weight estimation, respectively.

Iterative neural network adaptive robust control of a maglev planar motor with uncertainty compensation ability

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM.

OC-0635 Assessing the clinical impact of brachytherapy source tracking uncertainties of a novel IVD system

OC-0635 Assessing the clinical impact of brachytherapy source tracking uncertainties of a novel IVD system

Trajectory tracking control for quadrotor based on time-varying threshold event-triggered extended state observer

Considering the problems of unknown external disturbances and parametric uncertainties in trajectory tracking of a quadrotor, a backstepping control method based on time-varying threshold event-triggered extended state observer (TETESO) and a nonlinear differentiator with lead compensation is proposed in this paper. First, to facilitate the controller design, the quadrotor system is divided into the position subsystem and the attitude subsystem, which are transformed into the original cascaded dynamics. Second, a TETESO which can observe the unmeasurable states and the total disturbance as well as reduce the unnecessary communication resources between sensor and observer is proposed. It is proved that the observation errors are bounded, and Zeno behavior is avoided. Third, a new controller synthesized by the proposed TETESO and the backstepping control method is designed for the position subsystem and the attitude subsystem. To address the “differential explosion” problem in the backstepping control method, a nonlinear differentiator with lead compensation is used to compute the derivative of desired attitudes in the controller. Moreover, the stability analysis of the closed-loop system is proved and the proposed controller can ensure the ultimate boundedness of all signals. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier–Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.

Control of batch pulping process using data-driven constrained iterative learning control

Kraft pulping uses steam and chemicals to convert wood chips to pulp, used for production of paper, textile fiber or other cellulosic materials, in large batch or continuous digesters. Batch digesters owe their widespread use to lower capex as well as flexibility of operation that can easily accommodate changes in feedstock or product grades. However, control of batch pulping process is challenging since wood is a naturally-occurring reactant. Moreover, lack of real-time measurements of key performance indicator such as Kappa number and lack of high-fidelity models make the batch control difficult to implement. One solution consists of using run-to-run control that uses offline laboratory measurements to provide a recipe for the upcoming batch. In this work, a first-principles dynamic model is developed for manufacture of dissolving pulp for textile fiber from wood. Subsequently, a data-driven constrained Iterative learning control (cILC) technique is explored for Kappa number control via manipulation of recirculation liquor temperature. Data-driven cILC uses a linear time varying model of the batch pulping process that is developed and updated from historical data. Constraints are imposed on batch-to-batch change in the inputs to prevent sharp fluctuations of the process state and output variables between batches. The data-driven cILC solves a multivariable linear regression followed by a quadratic program to generate a batch recipe for the next run. Three case studies are presented to evaluate performances for target Kappa tracking, and deterministic and stochastic uncertainty handling. The results demonstrate that data-driven cILC is effective in control of the pulp digester, even in presence of uncertainties, without the need for expensive real-time measurements.

Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation

Abstract Modelling is widely used in ecology and its utility continues to increase as scientists, managers and policy‐makers face pressure to effectively manage ecosystems and meet conservation goals with limited resources. As the urgency to forecast ecosystem responses to global change grows, so do the number and complexity of predictive ecological models and the value of iterative prediction, both of which demand validation and cross‐model comparisons. This challenges ecologists to provide predictive models that are reusable, interoperable, transparent and able to accommodate updates to both data and algorithms. We propose a practical solution to this challenge based on the PERFICT principles (frequent Predictions and Evaluations of Reusable, Freely accessible, Interoperable models, built within Continuous workflows that are routinely Tested), using a modular and integrated framework. We present its general implementation across seven common components of ecological model applications—(i) the modelling toolkit; (ii) data acquisition and treatment; (iii) model parameterisation and calibration; (iv) obtaining predictions; (v) model validation; (vi) analysing and presenting model outputs; and (vii) testing model code—and apply it to two approaches used to predict species distributions: (1) a static statistical model, and (2) a complex spatiotemporally dynamic model. Adopting a continuous workflow enabled us to reuse our models in new study areas, update predictions with new data, and re‐parameterise with different interoperable modules using freely accessible data sources, all with minimal user input. This allowed repeating predictions and automatically evaluating their quality, while centralised inputs, parameters and outputs, facilitated ensemble forecasting and tracking uncertainty. Importantly, the integrated model validation promotes a continuous evaluation of the quality of more‐ or less‐parsimonious models, which is valuable in predictive ecological modelling. By linking all stages of an ecological modelling exercise, it is possible to overcome common challenges faced by ecological modellers, such as changing study areas, choosing between different modelling approaches, and evaluating the appropriateness of the model. This ultimately creates a more equitable and robust playing field for both modellers and end users (e.g. managers), and contributes to position predictive ecology as a central contributor to global change forecasting.

Attitude tracking control for quadrotor based on time-varying gain extended state observer

Considering the problems of unknown external disturbances and parametric uncertainties in attitude tracking of a quadrotor, a backstepping sliding mode control method based on the time-varying gain extended state observer is proposed in this article. First, the mathematical model of the attitude system of the quadrotor is introduced. Second, a time-varying gain extended state observer is proposed to observe the unmeasurable states, total disturbance, and reduce the “peaking value” phenomenon, which often occurs in traditional linear extended state observers. It is proved that the observation errors are bounded. Third, a novel controller synthesized by backstepping sliding mode control method and the time-varying gain extended state observer is proposed for the attitude system of the quadrotor. Moreover, the designed controller can ensure that all signals of the closed-loop system are bounded. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

Adaptive preassigned-time controller design for a hypersonic vehicle with improved performance and uncertainties

The tracking control problem of a hypersonic vehicle (HSV) under tracking error constraints and uncertainties is investigated. To achieve tracking error converges to its preselected constrained boundary in preassigned time, a performance function is proposed. The preassigned time can be determined by requirement of HSV in prior. Then an adaptive preassigned-time prescribed performance (PTPP) dynamic inversion controller and an adaptive PTPP backstepping controller are proposed for velocity and altitude subsystems, respectively. At the level of control design for altitude subsystem, a dynamic gain fixed-time filter (DGFTF) is proposed for solving ‘explosion of complexity’ problem. It can ensure that the filter error converges to an arbitrarily small vicinity of zero in fixed time. What is more, it is proved that the proposed PTPP controller can assure predefined performance of velocity and altitude tracking errors in a given preselected time. Finally, compared simulation is performed to validate the effectiveness of the proposed control strategy.

Controller design for optimal operation of Multiple Effect Evaporator of paper mills

The Kraft recovery process in the paper mills is highly complex and nonlinear. The evaporation of black liquor using Multiple Effect Evaporators (MEE) in the recovery unit of the Kraft process is an energy-consuming procedure. The challenge lies in designing a proficient control strategy to conserve energy and guarantee good product quality. In this article, the 2-DOF-PI controller is designed to enable efficient control of MEE and eliminate process uncertainties. The Heptads’ Effect Falling Film Evaporator in the backward feed flow configuration is used as a working platform. The steady-state and transient behavior of MEE is modeled and analyzed to assist in controller design. The steady-state process parameters, that guarantee optimum energy efficiency, are estimated using Moth–Flame Optimization (MFO). MFO is also employed to estimate the optimal 2-DOF-PI controller parameters to attain improved product quality and energy-efficient performance of MEE. The competence of MFO towards controller tuning is shown by a fair comparison with some well-known optimization techniques. The quantitative investigation of the statistical results demonstrates that MFO outperforms the other algorithms. To validate the proficiency of the designed control scheme, its performance is compared with basic PI/PID control strategy for set-point tracking, noise suppression, and process uncertainties. The simulation results indicate that the designed MFO-tuned 2-DOF-PI controller offers efficient control action and, therefore, proves to be an appropriate control algorithm to ensure sustainable production.

Passivity‐based control for a DC/DC high‐gain transformerless boost converter

AbstractIn this work, an adaptive passivity‐based controller for a DC‐DC high‐gain transformerlessdouble‐inductor boost converter is fully detailed. The proposed current‐mode control scheme results in two feedback loops, a current controller for tracking the inductor current considering damping injection and energy shaping, and a voltage loop composed by a proportional‐integral action to guarantee output voltage regulation. Furthermore, a parametric uncertainty estimator using immersion‐invariance approach is designed to improve the robustness of the current loop. As a result, a multi‐loop adaptive nonlinear energy‐based controller, which ensures regional asymptotic stability via Lyapunov analysis is accomplished. In addition, considering practical conditions, real‐time numerical simulations, using a 1 kW case‐study converter, are carried out in order to assess the effectiveness of the proposed control scheme. Results for output voltage regulation, current tracking, and parametric uncertainty estimation under input voltage and load step changes are shown.