Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed data. The training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local minima. In this thesis, a novel training method is proposed. This method is based on the relatively new Smooth Variable Structure filter (SVSF) and is formulated for feedforward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the Sliding Mode Concept and works in a predictor-corrector fashion. The SVSF applies a discontinuous corrective term to estimate state and parameters. Its advantages include guaranteed stability, robustness, and fast speed of convergence. The proposed training technique is applied to three real-world benchmark problems and to a fault detection application in a Ford diesel engine. SVSF-based training technique shows an excellent generalization capability and a fast speed of convergence. / Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed data. The training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local minima. In this thesis, a novel training method is proposed. This method is based on the relatively new Smooth Variable Structure filter (SVSF) and is formulated for feedforward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the Sliding Mode Concept and works in a predictor-corrector fashion. The SVSF applies a discontinuous corrective term to estimate state and parameters. Its advantages include guaranteed stability, robustness, and fast speed of convergence. The proposed training technique is applied to three real-world benchmark problems and to a fault detection application in a Ford diesel engine. SVSF-based training technique shows an excellent generalization capability and a fast speed of convergence. / Thesis / Master of Applied Science (MASc)

Arti cial neural networks (ANNs) are a powerful processing units inspired by the human brain. They can be used in many applications due to their pattern classi cation abilities, ability to model complex nonlinear input-output mappings, and their ability to adapt and learn. The relatively new Smooth Variable Structure Filter (SVSF) has recently been applied to the training of feedforward multilayered neural networks. It has shown to have good accuracy and a fast speed of convergence. In this thesis, an engine fault detection system using an ANN will be implemented. ANNs are used in engine fault detection due to the high-noise environment that engine operate in. Additionally the fault detection system must work while the engine is mounted in a vehicle, which provide additional sources of noise. The SVSF training method is evaluated and compared to other traditional training methods. Also di erent accelerometer types are compared to evaluate whether lower cost accelerometers can be used to keep the system cost down. The system is tested by inducing a missing spark fault, a fault that has a complex fault signature and is di cult to detect, especially in an engine with a high number of cylinders. / Thesis / Master of Applied Science (MASc)

The main idea of this thesis is to formulate the smooth variable structure filter (SVSF) for target tracking applications in the presence of measurement origin uncertainty. Tracking, by definition is the recursive estimation of the states of an unknown target from indirect, inaccurate and uncertain measurements. The measurement origin uncertainty introduces the data association problem to the tracking system. The SVSF estimation strategy was first presented in 2007. This filter is based on sliding mode concepts formulated in a predictor-corrector form. Essentially, the SVSF uses an existence subspace and smoothing boundary layer to bind the estimated state trajectory to within a subspace around the true trajectory. The SVSF is demonstrated to be robust to modeling uncertainties and provide extra measures of performance such as magnitude of the chattering signal. Therefore, with respect to specific nature of car tracking problems that involves modeling uncertainty, it was hypothesized that a robust estimation strategy such as the SVSF, would improve the performance of the tracking system and give more robust tracking results. Also, having the extra information provided by the SVSF strategy, i.e. the chattering magnitude signal, would lead to algorithms that could better account for measurement origin uncertainty in the context of the data association process. Further to these hypotheses, this research has focused on investigating the performance of the SVSF in the target tracking problems, advancing the development of the SVSF, and employing its characteristics to deal with data association problems. The performance of the SVSF, in its current form, can be improved when there is fewer measurements than states by using its error covariance in target tracking. As the first contribution in this research, the SVSF is formulated in the context of target tracking in clutter and combined with data association algorithms, resulting in the SVSF-based probabilistic data association (PDA) and joint probabilistic data association (JPDA) for non-maneuvering and maneuvering targets. The results are promising in the tracking scenarios with modeling uncertainties. Therefore, the thesis is then expanded by generalizing the covariance of the SVSF for the cases where the number of measurements is less than the number of states. The generalized covariance formulation is then used to derive a generalized variable boundary layer (GVBL) SVSF. This new derivation gives an estimation method that is optimal in the MMSE sense and in the meantime preserves the robustness of the SVSF. The proposed algorithm improves the performance measures and makes a more reliable tracking algorithm. This thesis explores the hypothesis that multiple target tracking performance can be substantially improved by including chattering information from SVSF-based filtering in the data association method. A Bayesian framework is used to formulate a new set of augmented association probabilities which include the chattering information. The simulation and experimental results demonstrate that the proposed augmented probabilistic data association improves the performance of the tracking system including maneuvering cars, in particular for highly cluttered environments. The derived methods are applied on simulations and also on real data from an experimental setup. This thesis is made up of a compilation of papers that include three conference papers and three journal papers. / Thesis / Doctor of Philosophy (PhD)

Kalman-type filtering methods are mostly designed based on exact knowledge of the system’s model with known parameters. In real applications, there may be considerable amount of uncertainties about the model structure, physical parameters, level of noise, and initial conditions. In order to overcome such difficulties, robust state estimation techniques are recommended. This PhD thesis presents a novel robust state estimation method that is referred to as the 2nd-order smooth variable structure filter (2nd-order SVSF) and satisfies the first and second order sliding conditions. It is an extension to the 1st-order SVSF introduced in 2007. In the 1st-order SVSF chattering is reduced by using a smoothing boundary layer; however, the 2nd-order SVSF alleviates chattering by preserving the second order sliding condition. It reduces the estimation error and its first difference until the existence boundary layer is reached. Then after, it guarantees that the estimation error and its difference remain bounded given bounded noise and modeling uncertainties. As such, the 2nd-order SVSF produces more accurate and smoother state estimates under highly uncertain conditions than the 1st-order version. The main issue with the 2nd-order SVSF is that it is not optimal in the mean square error sense. In order to overcome this issue, the dynamic 2nd-order SVSF is initially presented based on a dynamic sliding mode manifold. This manifold introduces a variable cut-off frequency coefficient that adjusts the filter bandwidth. An optimal derivation of the 2nd-order SVSF is then obtained by minimizing the state error covariance matrix with respect to the cut-off frequency matrix. An experimental setup of an electro-hydrostatic actuator is used to compare the performance of the 2nd-order SVSF and its optimal version with other estimation methods such as the Kalman filter and the 1st-order SVSF. Experiments confirm the superior performance of the 2nd-order SVSF given modeling uncertainties. / Thesis / Doctor of Philosophy (PhD)

Fault Detection and Diagnosis (FDD) constitutes an essential aspect of modern life, with far-reaching implications spanning various domains such as healthcare, maintenance of industrial machinery, and cybersecurity. A comprehensive approach to FDD entails addressing facets related to detection, invariance, isolation, identification, and supervision. In FDD, there are two main perspectives: model-based and data-driven approaches. This thesis centers on model-based methodologies, particularly within the context of control and industrial applications. It introduces novel estimation strategies aimed at enhancing computational efficiency, addressing fault discretization, and considering robustness in fault detection strategies. In cases where the system's behavior can vary over time, particularly in contexts like fault detection, presenting multiple scenarios is essential for accurately describing the system. This forms the underlying principle in Multiple Model Adaptive Estimation (MMAE) like well-established Interacting Multiple Model (IMM) strategy. In this research, an exploration of an efficient version of the IMM framework, named Updated IMM (UIMM), is conducted. UIMM is applied for the identification of irreversible faults, such as leakage and friction faults, within an Electro-Hydraulic Actuator (EHA). It reduces computational complexity and enhances fault detection and isolation, which is very important in real-time applications such as Fault-Tolerant Control Systems (FTCS). Employing robust estimation strategies such as the Smooth Variable Structure Filter (SVSF) in the filter bank of this algorithm will significantly enhance its performance, particularly in the presence of system uncertainties. To relax the irreversible assumption used in the UIMM algorithm and thereby expanding its application to a broader range of problems, the thesis introduces the Moving Window Interacting Multiple Model (MWIMM) algorithm. MWIMM enhances efficiency by focusing on a subset of possible models, making it particularly valuable for fault intensity and Remaining Useful Life (RUL) estimation. Additionally, this thesis delves into exploring chattering signals generated by the SVSF filter as potential indicators of system faults. Chattering, arising from model mismatch or faults, is analyzed for spectral content, enabling the identification of anomalies. The efficacy of this framework is verified through case studies, including the detection and measurement of leakage and friction faults in an Electro-Hydraulic Actuator (EHA). / Thesis / Candidate in Philosophy / In everyday life, from doctors diagnosing illnesses to mechanics inspecting cars, we encounter the need for fault detection and diagnosis (FDD). Advances in technology, like powerful computers and sensors, are making it possible to automate fault diagnosis processes and take corrective actions in real-time when something goes wrong. The first step in fault detection and diagnosis is to precisely identify system faults, ensuring they can be properly separated from normal variations caused by uncertainties, disruptions, and measurement errors. This thesis explores model-based approaches, which utilize prior knowledge about how a normal system behaves, to detect abnormalities or faults in the system. New algorithms are introduced to enhance the efficiency and flexibility of this process. Additionally, a new strategy is proposed for extracting information from a robust filter, when used for identifying faults in the system.

The fast-growing electric vehicles (EVs) market demands huge efforts from car manufacturers to develop and improve their current products’ systems. A fast charge of the battery pack is one of the challenges encountered due to the battery limitations regarding behaviour and additional degradation when exposed to such a rough situation. In addition, the outcome of a study performed on a battery does not apply to others, especially if their chemistries are different. Hence, extensive testing is required to understand the influence of design decisions on the particular energy storage device to be implemented. Due to batteries’ nonlinear behaviour that is highly dependent on external variables such as temperature, the dynamic load and aging, another defying task is the widely studied state of charge (SOC) estimation, commonly considered one of the most significant functions in a battery management system (BMS). This thesis presents an extensive battery fast charging aging test study equipped with promising current charging profiles from published literature to minimize aging. Four charging protocols are carefully designed to charge the cell from 10 to 80% SOC within fifteen minutes and have their performances discussed. A dual state estimation algorithm is modelled to estimate the SOC with the assistance of a capacity state of health (SOHcap) estimation. Finally, the dual state estimation model is validated with the fast charging aging test data. / Thesis / Master of Science in Mechanical Engineering (MSME)

Lithium-ion (Li-ion) batteries are amongst the most commonly used types in Electric (EVs) and Hybrid Electric (HEVs) Vehicles due to their high energy and power densities, as well as long lifetime. A battery is one of the most important components of an EV and hence it needs to be monitored and controlled accurately. The safety, and reliability of battery packs must then, be ensured by accurate management, control, and monitoring functions by using a Battery Management System (BMS). A BMS is also responsible for accurate real-time estimation of the State of Charge (SoC), State of Health (SoH) and State of Power (SoP) of the battery. The battery SoC provides information on the amount of energy left in the battery. The SoH determines the remaining capacity and health of a pack, and the SoP represents the maximum available power. These critical battery states cannot be directly measured. Therefore, they have to be inferred from measurable parameters such as the current delivered by the battery as well as its terminal voltage. Consequently, in order to offer accurate monitoring of SoC, SoH and SoP, advanced numerical estimation methods need to be deployed. In the estimation process, the states and parameters of a system are extracted from measurements. The objective is to reduce the estimation errors in the presence of uncertainties and noise under different operating conditions. This thesis uses and provides different enhancements to a robust estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) for condition monitoring of batteries. The SVSF is a predictor-corrector method based on sliding mode control that enhances the robustness in the presence of noise and uncertainties. The methods are proposed to provide accurate estimates of the battery states of operation and can be implemented in real-time in BMS. To improve the performance of battery condition monitoring, a measurement-based SoC estimation method called coulomb counting is paired with model-based state estimation strategy. Important considerations in parameter and state estimation are model formulation and observability. In this research, a new model formulation that treats coulomb counting as an added measurement is proposed. It is shown that this formulation enhanced information extraction, leading to a more accurate state estimation, as well as an increase in the number of parameters and variables that can be estimated while maintaining observability. This model formulation is used for characterizing the battery in a range of operating conditions. In turn, the models are integral to a proposed adaptive filter that is a combination of the Interacting Multiple Model (IMM) concept and the SVSF. It is shown that this combined strategy is an efficient estimation approach that can effectively deal with battery aging. The proposed method provides accurate estimation for various SoH of a battery. Further to battery aging adaptation, measurement errors such as sensor noise, drift, and bias that affect estimation performance, are considered. To improve the accuracy of battery state estimation, a noise covariance adaptation scheme is developed for the SVSF method. This strategy further improves the robustness of the SVSF in the presence of unknown physical disturbances, noise, and initial conditions. The proposed estimation strategies are also considered for their implementation on battery packs. An important consideration in pack level battery management is cell-to-cell variations that impact battery safety. This study considers online battery parametrization to update the pack’s model over time and to detect cell-to-cell variability in parallel-connected battery cells configurations. Experimental data are used to validate and test the efficacy of the proposed methods in this thesis. / Thesis / Doctor of Philosophy (PhD) / To address the critical issue of climate change, it is necessary to replace fossil-fuel vehicles with battery-powered electric vehicles. Despite the benefits of electric vehicles, their popularity is still limited by the range anxiety and the cost determined by the battery pack. The range of an electric vehicle is determined by the amount of charge in its battery pack. This is comparable to the amount of gasoline in a gasoline vehicle’s tank. In consideration of the need for methods to address range anxiety, it is necessary to develop advanced algorithms for continuous monitoring and control of a battery pack to maximize its performance. However, the amount of charge and health of a battery pack cannot be measured directly and must be inferred from measurable variables including current, voltage and temperature. This research presents several algorithms for detecting the range and health of a battery pack under a variety of operating conditions. With a more accurate algorithm, a battery pack can be monitored closely, resulting in lower long-term costs. Adaptive methods for determining a battery’s state of charge and health in uncertain and noisy conditions have been developed to provide an accurate measure of available charge and capacity. Methods are then extended to improve the determination of state of charge and health for a battery module.

Faulty manufactured alternators lead to commercial and safety concerns when installed in vehicles. Alternators have a major role in the Electrical Power Generation System (EPGS) of vehicles, and a defective alternator will lead to damaging of the battery and other important electric accessories. Therefore, fault detection and diagnosis of alternators can be implemented to quickly and accurately determine the health of an alternator during end of line testing, and not let faulty components leave the manufacturer. The focus of this research is to develop a Model Based Fault Detection and Diagnosis (FDD) strategy for detecting alternator faults during end of line testing. The proposed solution uses Extended Kalman Smooth Variable Structure Filter (EK-SVSF) to detect common alternator faults. A solution using the Dual Extended Kalman Filter (DEKF) is also discussed. The alternator faults were programmatically simulated on alternator measurements. The experimental results prove that both the EK-SVSF and DEKF strategies were very effective in alternator modeling and detecting open diode faults, shorted diode faults, and stator imbalance faults. / Thesis / Master of Applied Science (MASc)

Many problems in tracking control have been identified over the years, such as the availability of systems states, the presence of noise and system uncertainties, and speed of response, just to name a few. This thesis is concerned with developing novel integrated control and estimation algorithms to overcome some of these problems in order to achieve an efficient tracking performance. Since there are some significant advantages associated with Sliding Mode Control (SMC) or Variable Structure Control (VSC), (fast regulation rate and robustness to uncertainties), this research reviews and extends new filtering concepts for state estimation, referred to as the Variable Structure Filter (VSF)and Smooth Variable Structure Filter (SVSF). These are based on the philosophy of Sliding Mode Control.<p>The VSF filter is designed to estimate some of the states of a plant when noise and uncertainties are presented. This is accomplished by refining an estimate of the states in an iterative fashion using two filter gains, one based on a noiseless system with no uncertainties and the second gain which reflects these uncertainties. The VSF is combined seamlessly with the Sliding Mode Controller to produce an integrated controller called a Sliding Mode Controller and Filter (SMCF). This new controller is shown to be a robust and effective integrated control strategy for linear systems. For nonlinear systems, a novel integrated control strategy called the Smooth Sliding Mode Controller and Filter (SSMCF), fuses the SMC and SVSF in a particular form to address nonlinearities. The gain term in the SVSF is redefined to form a new algorithm called the SVSF with revised gain in order to obtain a better estimation performance. Its performance is compared to that of the Extended Kalman Filter (EKF) when applied to a particular nonlinear plant.<p>The SMCF and SSMCF are applied to the experimental prototype of a precision positioning hydraulic system called an ElectroHydraulic Actuator (EHA) system. The EHA system is known to display nonlinear characteristics but can approximate linear behavior under certain operating conditions, making it ideal to test the robustness of the proposed controllers.<p>The main conclusion drawn in this research was that the SMCF and SSMCF as developed and implemented, do exhibit robust and high performance state estimation and trajectory tracking control given modeling uncertainties and noise. The controllers were applied to a prototype EHA which demonstrated the use of the controllers in a real world application. It was also concluded that the application of the concepts of VSC for the controller can alleviate a challenging mechanical problem caused by a slip-stick characteristic in friction. Another conclusion is that the revised form of the SVSF could obtain robust and fast state estimation for nonlinear systems.<p>The original contributions of the research include: i) proposing the SMCF and SSMCF, ii) applying the Sliding Mode Controller to suppress cross-over oscillations caused by the slip-stick characteristics in friction which often occur in mechanical systems, iii) the first application of the SVSF for state estimation and iv) a comparative study of the SVSF and Extended Kalman Filter (EKF) to the EHA demonstrating the superiority of the SVSF for state estimation performance under both steady-state and transient conditions for the application considered.<p>The dissertation is written in a paper format unlike the traditional Ph.D thesis manuscript. The content of the thesis discourse is based on five manuscripts which are appended at the end of the thesis. Fundamental principles and concepts associated with SMC, VSF, SVSF and the fused controllers are introduced. For each paper, the objectives, approaches, typical results, conclusions and major contributions are presented. Major conclusions are summarized and original contributions reiterated.

Many problems in tracking control have been identified over the years, such as the availability of systems states, the presence of noise and system uncertainties, and speed of response, just to name a few. This thesis is concerned with developing novel integrated control and estimation algorithms to overcome some of these problems in order to achieve an efficient tracking performance. Since there are some significant advantages associated with Sliding Mode Control (SMC) or Variable Structure Control (VSC), (fast regulation rate and robustness to uncertainties), this research reviews and extends new filtering concepts for state estimation, referred to as the Variable Structure Filter (VSF)and Smooth Variable Structure Filter (SVSF). These are based on the philosophy of Sliding Mode Control.<p>The VSF filter is designed to estimate some of the states of a plant when noise and uncertainties are presented. This is accomplished by refining an estimate of the states in an iterative fashion using two filter gains, one based on a noiseless system with no uncertainties and the second gain which reflects these uncertainties. The VSF is combined seamlessly with the Sliding Mode Controller to produce an integrated controller called a Sliding Mode Controller and Filter (SMCF). This new controller is shown to be a robust and effective integrated control strategy for linear systems. For nonlinear systems, a novel integrated control strategy called the Smooth Sliding Mode Controller and Filter (SSMCF), fuses the SMC and SVSF in a particular form to address nonlinearities. The gain term in the SVSF is redefined to form a new algorithm called the SVSF with revised gain in order to obtain a better estimation performance. Its performance is compared to that of the Extended Kalman Filter (EKF) when applied to a particular nonlinear plant.<p>The SMCF and SSMCF are applied to the experimental prototype of a precision positioning hydraulic system called an ElectroHydraulic Actuator (EHA) system. The EHA system is known to display nonlinear characteristics but can approximate linear behavior under certain operating conditions, making it ideal to test the robustness of the proposed controllers.<p>The main conclusion drawn in this research was that the SMCF and SSMCF as developed and implemented, do exhibit robust and high performance state estimation and trajectory tracking control given modeling uncertainties and noise. The controllers were applied to a prototype EHA which demonstrated the use of the controllers in a real world application. It was also concluded that the application of the concepts of VSC for the controller can alleviate a challenging mechanical problem caused by a slip-stick characteristic in friction. Another conclusion is that the revised form of the SVSF could obtain robust and fast state estimation for nonlinear systems.<p>The original contributions of the research include: i) proposing the SMCF and SSMCF, ii) applying the Sliding Mode Controller to suppress cross-over oscillations caused by the slip-stick characteristics in friction which often occur in mechanical systems, iii) the first application of the SVSF for state estimation and iv) a comparative study of the SVSF and Extended Kalman Filter (EKF) to the EHA demonstrating the superiority of the SVSF for state estimation performance under both steady-state and transient conditions for the application considered.<p>The dissertation is written in a paper format unlike the traditional Ph.D thesis manuscript. The content of the thesis discourse is based on five manuscripts which are appended at the end of the thesis. Fundamental principles and concepts associated with SMC, VSF, SVSF and the fused controllers are introduced. For each paper, the objectives, approaches, typical results, conclusions and major contributions are presented. Major conclusions are summarized and original contributions reiterated.