ali wadi | American University of Sharjah (original) (raw)

Papers by ali wadi

Research paper thumbnail of A novel localization-free approach to system identification for underwater vehicles using a Universal Adaptive Stabilizer

Research paper thumbnail of Probabilistic Voltage Fault Correction Method for Lithium-ion Batteries Using a Decentralized Cell Voltage Measurement Approach

IEEE Transactions on Vehicular Technology

This paper proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) ba... more This paper proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) battery cells to identify faulty sensor(s). The proposed method is based on a Bayesian probabilistic approach that detects possible measurement bias in any battery cell in real-time. A hypothesis bank is constructed for possible bias magnitudes in each cell. Subsequently, the fault detection algorithm computes the probability associated with all hypotheses. Once the probability of a certain hypothesis converges to unity, the faulty sensor and its associated bias are identified. The quantified bias can then be compensated in the measurement model of the associated cell. Details on the proposed method followed by experimental verification using commercial lithium-ion battery datasets are provided.

Research paper thumbnail of An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

IEEE Access

This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the ex... more This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell's dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented. INDEX TERMS Extended Kalman filter, invariant extended Kalman filter, EKF, IEKF.

Research paper thumbnail of Partial Discharge Localization in Power Transformers Using Invariant Extended Kalman Filter

IEEE Transactions on Instrumentation and Measurement

This article proposes a novel invariant extended Kalman filter (IEKF), a recently modified versio... more This article proposes a novel invariant extended Kalman filter (IEKF), a recently modified version of the extended Kalman filter (EKF), to estimate partial discharge (PD) location in a transformer insulation system model. An acoustic signal measurement is utilized to localize the PD location. Unlike conventional EKF methods, where the correction term used to update the state is linearly proportional to the output error, the correction term of the proposed algorithm is independent of the output error, resulting in a fast response with a significant reduction in the estimation error. In contrast to the EKF, the proposed method can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the PD's dynamics, the IEKF will still sustain a reasonable performance. In contrast, conventional EKFs can easily diverge if a mismatch between the measurement model and the true measurement occurs. Experimental results are shown to verify the proposed method's performance compared to a recently published variant of the EKF.

Research paper thumbnail of Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter

Energies, May 19, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of An Enhanced Fusion Algorithm With Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

IEEE-ASME Transactions on Mechatronics, 2023

Research paper thumbnail of Novel Design of a Hybrid Drone System for Cleaning Solar Panels

2022 Advances in Science and Engineering Technology International Conferences (ASET), 2022

Research paper thumbnail of Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

Research paper thumbnail of An Adaptive Autotuned Polynomial-Based Extended Kalman Filter for Sensorless Surface Temperature Estimation of Li-ion Battery Cells

IEEE Access

This paper proposes an adaptive filter to estimate the surface temperature of lithium-ion battery... more This paper proposes an adaptive filter to estimate the surface temperature of lithium-ion battery cells in real time. The proposed method aims to achieve a highly accurate temperature estimation at a relatively low implementation cost. For reliable sensorless temperature estimation, an accurate battery cell's temperature dynamic model and a measurement model relating the surface temperature to the battery states must be employed. These system's dynamic and measurement models are derived using polynomial curve fitting and implemented in the proposed adaptive autotuned extended Kalman filter (EKF). Derivation of the proposed technique followed by experimental verification are demonstrated. The proposed model and estimation algorithm are verified experimentally using two independent lithium-ion battery cell datasets. INDEX TERMS Battery management system, extended Kalman filter, lithium-ion battery, polynomial fitting, electric vehicle.

Research paper thumbnail of Enhanced EKF Method for State-of-Charge Estimation of Electric Vehicles' Li-ion Batteries under Highly Dynamic Power Profiles

2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

Research paper thumbnail of Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle

A Master of Science thesis in Mechanical Engineering by Ali H M Wadi entitled, "Modeling and... more A Master of Science thesis in Mechanical Engineering by Ali H M Wadi entitled, "Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle," submitted in December 2017. Thesis advisor is Dr. Jin-Hyuk Lee and thesis co-advisor is Dr. Shayok Mukhopadhyay. Soft and hard copy available.

Research paper thumbnail of Modeling and system identification of an autonomous underwater vehicle

2018 11th International Symposium on Mechatronics and its Applications (ISMA), 2018

This work aims to derive the kinematic and dynamic models governing the motion of an Autonomous U... more This work aims to derive the kinematic and dynamic models governing the motion of an Autonomous Underwater Vehicle (AUV) and identify the hydrodynamic parameters of a prototype AUV. Good knowledge of such models enable the design of controllers for AUVs to perform path following or trajectory tracking tasks. The modeling process takes into account the primary hydrodynamic phenomena that affect the vehicle, including drag, restoring forces, and added inertia. Quaternions are employed to describe the attitude of the vehicle to avoid the singularity associated with the Euler angles based formulation. The finite element analysis package ANSYS is used to identify the drag and added inertia parameters that affect the vehicle. The approach taken in the system identification task yields results that match well with published analytical and empirical results.

Research paper thumbnail of An Extended Kalman Filter with Exponential Thermoelectric Measurement Model for Sensorless Surface Temperature Estimation of Li-ion Batteries

2021 IEEE Energy Conversion Congress and Exposition (ECCE)

Research paper thumbnail of Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

IEEE Transactions on Vehicular Technology

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradat... more Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system.

Research paper thumbnail of Nonlinear sliding mode control of the Furuta pendulum

2018 11th International Symposium on Mechatronics and its Applications (ISMA)

This paper deals with the control design of a nonlinear Sliding Mode Controller (SMC) for an unde... more This paper deals with the control design of a nonlinear Sliding Mode Controller (SMC) for an underactuated mechanical system under external disturbance. The Furuta Pendulum represents a testbed on which control methodology is tested because it exhibits chaotic behavior, it involves highly nonlinear dynamics, and it is underactuated. The aforementioned remarks make the control of the Furuta pendulum a nontrivial task. The SMC methodology which uses the full model of the system, applied here, is pegged against a linear state feedback control law which is based on the linearized model around the control objective. The control laws are tested in a simulated environment in which external disturbances are injected, and the performance of the approaches is then compared.

Research paper thumbnail of Multi-model Investigation and Adaptive Estimation of the Acoustic Release of a Model Drug from Liposomes

IEEE Transactions on NanoBioscience

This paper researches a suitable mathematical model that can reliably predict the release of a mo... more This paper researches a suitable mathematical model that can reliably predict the release of a model drug (namely calcein) from biologically targeted liposomal nanocarriers triggered by ultrasound. Using mathematical models, curve fitting is performed on a set of five experimental acoustic drug release runs from Albumin-, Estrone-, and RGD-based Drug Delivery Systems (DDS). The three moieties were chosen to target specific cancers using receptor-mediated endocytosis. The best-fitting mathematical model is then enhanced using a Kalman filtering (KF) algorithm to account for the statistics of the dynamic and measurements noise sequences in predicted drug release. Unbiased drug-release estimates are realized by implementing an online noise identification algorithm. The algorithm is first deployed in a simulated environment in which it was rigorously tested and compared with the correct solution. Then, the algorithm was used to process the five experimental datasets. The results suggest that the Adaptive Kalman Filter (AKF) is exceptionally good at handling drug release estimation problems with a priori unknown or with changing noise covariances. In comparison with the KF, the AKF approach exhibited as low as a 69% reduction in the level of error in estimating the drug release state. Finally, the proposed algorithm is not computationally demanding and is capable of online estimation tasks.

Research paper thumbnail of A novel disturbance-robust adaptive trajectory tracking controller for a class of underactuated autonomous underwater vehicles

Ocean Engineering

Abstract This work describes an adaptive trajectory tracking controller for underactuated underwa... more Abstract This work describes an adaptive trajectory tracking controller for underactuated underwater vehicles. The control design process is two-fold; first, a high-level kinematic controller is designed to produce velocity commands that steer the vehicle towards the desired trajectory, and second, a low-level controller is designed to utilize the mathematical model of the vehicle to produce force and torque commands for the thrusters onboard the underwater vehicle. A novel adaptive Nussbaum-function-based controller is proposed and compared with an adaptive proportional controller with integral feedback. Further, a conditional adaptation scheme is developed to combat effects of noise, disturbance, or uncertainty. The proposed scheme governs adaptation such that the gains only adapt when it is necessary, even in the presence of noise. The target vehicle for the controller is a four-thruster quadcopter-like vehicle, which does not have direct control over all degrees of freedom. The issue of having an underactuated thruster arrangement is common in many classes of inspection robots. The devised adaptive control law exhibits fast convergence for the adaptation. Four different parametrized trajectories are tested, and the performance of the proposed algorithms, when considering the tracking errors and required control effort, is shown to be superior to an adaptive proportional controller.

Research paper thumbnail of Mitigating the Effect of Noise Uncertainty in the Online State-of-Charge Estimation of Li-ion Battery Cells

IEEE Transactions on Vehicular Technology

Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended... more Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended Kalman filter (EKF) has been successfully deployed in SOC estimation allowing real-time SOC monitoring. However, modeling inaccuracies, measurement faults, and wrong initialization can cause the estimation algorithm to diverge. The precise knowledge of statistical information about process and measurements noise is crucial for accurate system modeling and estimation. This paper presents a novel SOC estimation approach based on maximum-likelihood estimation (MLE). The process and measurements models are transformed to an error state propagation system where the innovation covariance is utilized to maximize the likelihood of the multivariate innovation distribution with respect to process and measurement covariances. The MLE formulation allows the estimation of the process and measurement noise covariance magnitudes, which are used to obtain an optimal SOC estimate. The proposed method is validated experimentally using a number of Li-ion battery cells under various testing conditions. The estimation performance is compared with that of the conventional EKF technique as well as previously published results based on autocovariance least-squares measurements noise estimation. The results indicate an enhanced performance for the new algorithm over the traditional EKF across all conducted tests.

Research paper thumbnail of Modeling and Bias-Robust Estimation of the Acoustic Release of Chemotherapeutics from Liposomes

Journal of Biomedical Nanotechnology

This paper models the acoustic drug release of chemotherapeutics from liposomes using a kinetic m... more This paper models the acoustic drug release of chemotherapeutics from liposomes using a kinetic model that accounts for systematic biases affecting the drug delivery process. An optimal stochastic filter is then proposed to provide robust estimates of the percent drug released. Optimality is guaranteed by accurately identifying the underlying statistical noise characteristics in experimental data. The estimator also quantifies the bias in the release, exhibited by the experimental data. Drug release is experimentally measured as a change in fluorescence upon the application of ultrasound. First, a first-order kinetic model is proposed to model the release, which is aided by a bias term to account for the fact that full release is not achieved under the conditions explored in this study. The noise structure affecting the process dynamics and the measurement process is then identified in terms of the statistical covariance of the measured quantities. The identified covariance magnitudes are then utilized to estimate the dynamics of drug release as well as the bias term. The identified a priori knowledge is used to implement an optimal Kalman filter, which was initially tested in a simulation environment. The experimental datasets are then fed into the filter to estimate the state and identify the bias. Experiments span a number of ultrasonic power densities for liposomes. The results suggest that the proposed algorithm, the optimal Kalman filter, performs well in modeling acoustically activated drug release from liposomes.

Research paper thumbnail of Accurate Estimation of Partial Discharge Location using Maximum Likelihood

IEEE Sensors Letters

In this article, a novel algorithm for high-accuracy partial discharge (PD) localization in an oi... more In this article, a novel algorithm for high-accuracy partial discharge (PD) localization in an oil insulation system is proposed. This study aims to identify the statistics of the dynamics and measurement noise sequences in a PD localization system and employ that information in realizing better estimates. An extended Kalman filter (EKF) is used to estimate the PD location. The performance of the filter is enhanced by identifying the true statistics of the noise sequences using a maximum likelihood estimation approach. The accuracy of the proposed optimal algorithm is verified experimentally by estimating the PD location in an oil insulation system under two experimental settings.

Research paper thumbnail of A novel localization-free approach to system identification for underwater vehicles using a Universal Adaptive Stabilizer

Research paper thumbnail of Probabilistic Voltage Fault Correction Method for Lithium-ion Batteries Using a Decentralized Cell Voltage Measurement Approach

IEEE Transactions on Vehicular Technology

This paper proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) ba... more This paper proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) battery cells to identify faulty sensor(s). The proposed method is based on a Bayesian probabilistic approach that detects possible measurement bias in any battery cell in real-time. A hypothesis bank is constructed for possible bias magnitudes in each cell. Subsequently, the fault detection algorithm computes the probability associated with all hypotheses. Once the probability of a certain hypothesis converges to unity, the faulty sensor and its associated bias are identified. The quantified bias can then be compensated in the measurement model of the associated cell. Details on the proposed method followed by experimental verification using commercial lithium-ion battery datasets are provided.

Research paper thumbnail of An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

IEEE Access

This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the ex... more This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell's dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented. INDEX TERMS Extended Kalman filter, invariant extended Kalman filter, EKF, IEKF.

Research paper thumbnail of Partial Discharge Localization in Power Transformers Using Invariant Extended Kalman Filter

IEEE Transactions on Instrumentation and Measurement

This article proposes a novel invariant extended Kalman filter (IEKF), a recently modified versio... more This article proposes a novel invariant extended Kalman filter (IEKF), a recently modified version of the extended Kalman filter (EKF), to estimate partial discharge (PD) location in a transformer insulation system model. An acoustic signal measurement is utilized to localize the PD location. Unlike conventional EKF methods, where the correction term used to update the state is linearly proportional to the output error, the correction term of the proposed algorithm is independent of the output error, resulting in a fast response with a significant reduction in the estimation error. In contrast to the EKF, the proposed method can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the PD's dynamics, the IEKF will still sustain a reasonable performance. In contrast, conventional EKFs can easily diverge if a mismatch between the measurement model and the true measurement occurs. Experimental results are shown to verify the proposed method's performance compared to a recently published variant of the EKF.

Research paper thumbnail of Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter

Energies, May 19, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of An Enhanced Fusion Algorithm With Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

IEEE-ASME Transactions on Mechatronics, 2023

Research paper thumbnail of Novel Design of a Hybrid Drone System for Cleaning Solar Panels

2022 Advances in Science and Engineering Technology International Conferences (ASET), 2022

Research paper thumbnail of Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

Research paper thumbnail of An Adaptive Autotuned Polynomial-Based Extended Kalman Filter for Sensorless Surface Temperature Estimation of Li-ion Battery Cells

IEEE Access

This paper proposes an adaptive filter to estimate the surface temperature of lithium-ion battery... more This paper proposes an adaptive filter to estimate the surface temperature of lithium-ion battery cells in real time. The proposed method aims to achieve a highly accurate temperature estimation at a relatively low implementation cost. For reliable sensorless temperature estimation, an accurate battery cell's temperature dynamic model and a measurement model relating the surface temperature to the battery states must be employed. These system's dynamic and measurement models are derived using polynomial curve fitting and implemented in the proposed adaptive autotuned extended Kalman filter (EKF). Derivation of the proposed technique followed by experimental verification are demonstrated. The proposed model and estimation algorithm are verified experimentally using two independent lithium-ion battery cell datasets. INDEX TERMS Battery management system, extended Kalman filter, lithium-ion battery, polynomial fitting, electric vehicle.

Research paper thumbnail of Enhanced EKF Method for State-of-Charge Estimation of Electric Vehicles' Li-ion Batteries under Highly Dynamic Power Profiles

2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

Research paper thumbnail of Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle

A Master of Science thesis in Mechanical Engineering by Ali H M Wadi entitled, "Modeling and... more A Master of Science thesis in Mechanical Engineering by Ali H M Wadi entitled, "Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle," submitted in December 2017. Thesis advisor is Dr. Jin-Hyuk Lee and thesis co-advisor is Dr. Shayok Mukhopadhyay. Soft and hard copy available.

Research paper thumbnail of Modeling and system identification of an autonomous underwater vehicle

2018 11th International Symposium on Mechatronics and its Applications (ISMA), 2018

This work aims to derive the kinematic and dynamic models governing the motion of an Autonomous U... more This work aims to derive the kinematic and dynamic models governing the motion of an Autonomous Underwater Vehicle (AUV) and identify the hydrodynamic parameters of a prototype AUV. Good knowledge of such models enable the design of controllers for AUVs to perform path following or trajectory tracking tasks. The modeling process takes into account the primary hydrodynamic phenomena that affect the vehicle, including drag, restoring forces, and added inertia. Quaternions are employed to describe the attitude of the vehicle to avoid the singularity associated with the Euler angles based formulation. The finite element analysis package ANSYS is used to identify the drag and added inertia parameters that affect the vehicle. The approach taken in the system identification task yields results that match well with published analytical and empirical results.

Research paper thumbnail of An Extended Kalman Filter with Exponential Thermoelectric Measurement Model for Sensorless Surface Temperature Estimation of Li-ion Batteries

2021 IEEE Energy Conversion Congress and Exposition (ECCE)

Research paper thumbnail of Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

IEEE Transactions on Vehicular Technology

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradat... more Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system.

Research paper thumbnail of Nonlinear sliding mode control of the Furuta pendulum

2018 11th International Symposium on Mechatronics and its Applications (ISMA)

This paper deals with the control design of a nonlinear Sliding Mode Controller (SMC) for an unde... more This paper deals with the control design of a nonlinear Sliding Mode Controller (SMC) for an underactuated mechanical system under external disturbance. The Furuta Pendulum represents a testbed on which control methodology is tested because it exhibits chaotic behavior, it involves highly nonlinear dynamics, and it is underactuated. The aforementioned remarks make the control of the Furuta pendulum a nontrivial task. The SMC methodology which uses the full model of the system, applied here, is pegged against a linear state feedback control law which is based on the linearized model around the control objective. The control laws are tested in a simulated environment in which external disturbances are injected, and the performance of the approaches is then compared.

Research paper thumbnail of Multi-model Investigation and Adaptive Estimation of the Acoustic Release of a Model Drug from Liposomes

IEEE Transactions on NanoBioscience

This paper researches a suitable mathematical model that can reliably predict the release of a mo... more This paper researches a suitable mathematical model that can reliably predict the release of a model drug (namely calcein) from biologically targeted liposomal nanocarriers triggered by ultrasound. Using mathematical models, curve fitting is performed on a set of five experimental acoustic drug release runs from Albumin-, Estrone-, and RGD-based Drug Delivery Systems (DDS). The three moieties were chosen to target specific cancers using receptor-mediated endocytosis. The best-fitting mathematical model is then enhanced using a Kalman filtering (KF) algorithm to account for the statistics of the dynamic and measurements noise sequences in predicted drug release. Unbiased drug-release estimates are realized by implementing an online noise identification algorithm. The algorithm is first deployed in a simulated environment in which it was rigorously tested and compared with the correct solution. Then, the algorithm was used to process the five experimental datasets. The results suggest that the Adaptive Kalman Filter (AKF) is exceptionally good at handling drug release estimation problems with a priori unknown or with changing noise covariances. In comparison with the KF, the AKF approach exhibited as low as a 69% reduction in the level of error in estimating the drug release state. Finally, the proposed algorithm is not computationally demanding and is capable of online estimation tasks.

Research paper thumbnail of A novel disturbance-robust adaptive trajectory tracking controller for a class of underactuated autonomous underwater vehicles

Ocean Engineering

Abstract This work describes an adaptive trajectory tracking controller for underactuated underwa... more Abstract This work describes an adaptive trajectory tracking controller for underactuated underwater vehicles. The control design process is two-fold; first, a high-level kinematic controller is designed to produce velocity commands that steer the vehicle towards the desired trajectory, and second, a low-level controller is designed to utilize the mathematical model of the vehicle to produce force and torque commands for the thrusters onboard the underwater vehicle. A novel adaptive Nussbaum-function-based controller is proposed and compared with an adaptive proportional controller with integral feedback. Further, a conditional adaptation scheme is developed to combat effects of noise, disturbance, or uncertainty. The proposed scheme governs adaptation such that the gains only adapt when it is necessary, even in the presence of noise. The target vehicle for the controller is a four-thruster quadcopter-like vehicle, which does not have direct control over all degrees of freedom. The issue of having an underactuated thruster arrangement is common in many classes of inspection robots. The devised adaptive control law exhibits fast convergence for the adaptation. Four different parametrized trajectories are tested, and the performance of the proposed algorithms, when considering the tracking errors and required control effort, is shown to be superior to an adaptive proportional controller.

Research paper thumbnail of Mitigating the Effect of Noise Uncertainty in the Online State-of-Charge Estimation of Li-ion Battery Cells

IEEE Transactions on Vehicular Technology

Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended... more Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended Kalman filter (EKF) has been successfully deployed in SOC estimation allowing real-time SOC monitoring. However, modeling inaccuracies, measurement faults, and wrong initialization can cause the estimation algorithm to diverge. The precise knowledge of statistical information about process and measurements noise is crucial for accurate system modeling and estimation. This paper presents a novel SOC estimation approach based on maximum-likelihood estimation (MLE). The process and measurements models are transformed to an error state propagation system where the innovation covariance is utilized to maximize the likelihood of the multivariate innovation distribution with respect to process and measurement covariances. The MLE formulation allows the estimation of the process and measurement noise covariance magnitudes, which are used to obtain an optimal SOC estimate. The proposed method is validated experimentally using a number of Li-ion battery cells under various testing conditions. The estimation performance is compared with that of the conventional EKF technique as well as previously published results based on autocovariance least-squares measurements noise estimation. The results indicate an enhanced performance for the new algorithm over the traditional EKF across all conducted tests.

Research paper thumbnail of Modeling and Bias-Robust Estimation of the Acoustic Release of Chemotherapeutics from Liposomes

Journal of Biomedical Nanotechnology

This paper models the acoustic drug release of chemotherapeutics from liposomes using a kinetic m... more This paper models the acoustic drug release of chemotherapeutics from liposomes using a kinetic model that accounts for systematic biases affecting the drug delivery process. An optimal stochastic filter is then proposed to provide robust estimates of the percent drug released. Optimality is guaranteed by accurately identifying the underlying statistical noise characteristics in experimental data. The estimator also quantifies the bias in the release, exhibited by the experimental data. Drug release is experimentally measured as a change in fluorescence upon the application of ultrasound. First, a first-order kinetic model is proposed to model the release, which is aided by a bias term to account for the fact that full release is not achieved under the conditions explored in this study. The noise structure affecting the process dynamics and the measurement process is then identified in terms of the statistical covariance of the measured quantities. The identified covariance magnitudes are then utilized to estimate the dynamics of drug release as well as the bias term. The identified a priori knowledge is used to implement an optimal Kalman filter, which was initially tested in a simulation environment. The experimental datasets are then fed into the filter to estimate the state and identify the bias. Experiments span a number of ultrasonic power densities for liposomes. The results suggest that the proposed algorithm, the optimal Kalman filter, performs well in modeling acoustically activated drug release from liposomes.

Research paper thumbnail of Accurate Estimation of Partial Discharge Location using Maximum Likelihood

IEEE Sensors Letters

In this article, a novel algorithm for high-accuracy partial discharge (PD) localization in an oi... more In this article, a novel algorithm for high-accuracy partial discharge (PD) localization in an oil insulation system is proposed. This study aims to identify the statistics of the dynamics and measurement noise sequences in a PD localization system and employ that information in realizing better estimates. An extended Kalman filter (EKF) is used to estimate the PD location. The performance of the filter is enhanced by identifying the true statistics of the noise sequences using a maximum likelihood estimation approach. The accuracy of the proposed optimal algorithm is verified experimentally by estimating the PD location in an oil insulation system under two experimental settings.