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In industries where harsh environments and stringent safety requirements are prevalent, the widespread use of applications has made it essential to focus on fault detection and diagnosis (FDD) in hydraulic actuators. To achieve this, model-based FDD techniques are utilized, which employ estimation tools like observers and filters. However, for many applications, particularly in fluid power systems, observability, and parameter uncertainty pose constraints to extracting information and estimating parameters. To address these issues, an efficient form of interactive multiple model (IMM), called updated IMM (UIMM), is applied to an electro-hydraulic actuator (EHA) to detect and isolate persisting friction and leakage faults. UIMM method progresses through a series of models that correspond to the fault condition's progression instead of considering all models at once (as is done in IMM). This reduces the number of models running simultaneously, providing two significant benefits: enhanced computational efficiency and avoidance of the combinatorial explosion. The smooth variable structure filter with variable boundary layer (SVSF-VBL) is used for state and parameter estimation in conjunction with UIMM. SVSF-VBL is a reliable suboptimal estimation method that performs better than the Kalman filter regarding uncertainties related to the system and modeling. The performance of the UIMM method is validated by the simulation of fault conditions for a typical EHA. A fault tolerable control system (FTCS) has been designed to demonstrate the use of the proposed FDD strategy for fault management in a closed-loop system. [ABSTRACT FROM AUTHOR]

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV). [ABSTRACT FROM AUTHOR]

An important area of study for aerospace and electronic systems involves target tracking applications. To successfully track a target, state and parameter estimation strategies are used in conjunction with data association techniques. Even after 50 years, the Kalman filter (KF) remains the most popular and well-studied estimation strategy in the field. However, the KF adheres to a number of strict assumptions that leads to instabilities in some cases. The smooth variable structure filter (SVSF) is a relatively new method, which is becoming increasingly popular due to its robustness to disturbances and uncertainties. This paper presents a new formulation of the SVSF. The probabilistic and joint probabilistic data association techniques are combined with the SVSF and applied on multitarget tracking scenarios. In addition, a new covariance formulation of the SVSF is presented based on improving the estimation results of nonmeasured states. The results are compared and discussed with the popular KF method. [ABSTRACT FROM AUTHOR]

The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are among the most popular estimation methods. The smooth variable structure filter (SVSF) is a relatively new sliding mode estimator. In an effort to use the accuracy of the EKF and the UKF and the robustness of the SVSF, the filters have been combined, resulting in two new estimation strategies, called the EK-SVSF and the UK-SVSF, respectively. The algorithms were validated by testing them on a well-known target tracking computer experiment. [ABSTRACT FROM AUTHOR]

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery's SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2 % over the full operating range of SoC along with an accurate estimation of SoH. [ABSTRACT FROM AUTHOR]

This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observa-tions. These two target tracking problems provide a good bench-mark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its sta-bility and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters per-formances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accu-racy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlin-ear target tracking problems, which in the past have typically been solved by Kalman or particle filters. [ABSTRACT FROM AUTHOR]

This article presents the development of a multiple inner-loop control strategy for improving the performance of hydrostatic actuation systems. In these actuators, the presence of nonlinearities associated with pump/motor static friction and backlash, pressure drop in the piping system, and nonlinear friction at the load have a significant effect on the performance and positional accuracy of the system. The effect of nonlinear friction at the pump/motor interface has been overcome by the use of a high gain pump-speed inner-loop control strategy. In this article, the concept of inner-loop control will be extended to target other specific sources of performance degradation. Velocity feedback will be incorporated in this manner to decrease the effects of pump backlash and nonlinear friction at the load. Simulation results supported by theoretical analysis indicate that a considerable improvement in performance can be achieved by the implementation of this control strategy. [ABSTRACT FROM AUTHOR]

The article presents a proposal for a neural network-based control system for fine control of the intake air/fuel ratio in a bi-fuel engine. The control system is a module for an existing vehicle manufacturer's electronic control units (ECUs). Effectiveness of the neural control system is demonstrated by a simulation of a Dodge four-stroke bi-fuel engine.

Highlights • Robust estimation strategies are critical for the safe and reliable control of mechanical and electrical systems. • Presents a novel robust estimation method based on sliding mode concepts and the smooth variable structure filter (SVSF). • A mathematical proof of stability is provided based on Lyapunov theory. • The proposed nonlinear second‐order (NSO) filtering strategy is applied on an aerospace actuator. • The results are compared with the popular Kalman and extended Kalman filter, as well as the SVSF. Abstract In this paper, a new strategy referred to as the nonlinear second-order (NSO) filter is presented and used for estimation of linear and nonlinear systems in the presence of uncertainties. Similar to the popular Kalman filter estimation strategy, the proposed strategy is model-based and formulated as a predictor-corrector. The NSO filter is based on variable structure theory that utilizes a switching term and gain that ensures some level of estimation stability. It offers improvements in terms of robustness to modeling uncertainties and errors. The proof of stability is derived based on Lyapunov that demonstrates convergence of estimates towards the true state values. The proposed filtering strategy is based on a second-order Markov process that utilizes information from the current and past two time steps. An experimental system was setup and characterized in order to demonstrate the proposed filtering strategy's performance. The strategy was compared with the popular Kalman filter (and its nonlinear form) and the smooth variable structure filter (SVSF). Experimental results demonstrate that the proposed nonlinear second-order filter provides improvements in terms of state estimation accuracy and robustness to modeling uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

Abstract Deep-Learning has become a leading strategy for artificial intelligence and is being applied in many fields due to its excellent performance that has surpassed human cognitive abilities in a number of classification and control problems (Ciregan, Meier, & Schmidhuber, 2012; Mnih et al., 2015). However, the training process of Deep-Learning is usually slow and requires high-performance computing, capable of handling large datasets. The optimization of the training method can improve the learning rate of the Deep-Learning networks and result in a higher performance while using the same number of training epochs (cycles). This paper considers the use of estimation theory for training of large neural networks and in particular Deep-Learning networks. Two estimation strategies namely the Extended Kalman Filter (EKF) and the Smooth Variable Structure Filter (SVSF) have been revised (subsequently referred to as RSVSF and REKF) and used for network training. They are applied to several benchmark datasets and comparatively evaluated. Highlights • Two Deep-Learning training algorithms are introduced, called REKF and RSVSF. • REKF and RSVSF allow Deep-Learning networks to learn fast. • REKF and RSVSF use less memory than current adaptive methods. [ABSTRACT FROM AUTHOR]

Model-order reduction and minimization of the CPU run-time while maintaining the model accuracy are critical requirements for real-time implementation of lithium-ion electrochemical battery models. In this paper, an isothermal, continuous, piecewise-linear, electrode-average model is developed by using an optimal knot placement technique. The proposed model reduces the univariate nonlinear function of the electrode's open circuit potential dependence on the state of charge to continuous piecewise regions. The parameterization experiments were chosen to provide a trade-off between extensive experimental characterization techniques and purely identifying all parameters using optimization techniques. The model is then parameterized in each continuous, piecewise-linear, region. Applying the proposed technique cuts down the CPU run-time by around 20%, compared to the reduced-order, electrode-average model. Finally, the model validation against real-time driving profiles (FTP-72, WLTP) demonstrates the ability of the model to predict the cell voltage accurately with less than 2% error. [ABSTRACT FROM AUTHOR]

A method that is often used for parameter estimation is the extended Kalman filter (EKF). EKF is a model-based strategy that implicitly considers the effect of modeling uncertainties. This implicit consideration often leads to the tuning of the filter by trial and error. When formulated for parameter estimation, the "tuned" EKF becomes sensitive to uncertainties in its internal model. The EKF's robustness can be improved by combining it with the recently proposed variable structure filter (VSF) concept. In a combined form, the modeling uncertainties no longer affect stability, but impact the performance and the quality of the estimation process. Furthermore, the VSF concept provides a secondary set of indicators of performance that is in addition to the estimation error and that pertains to the range of parametric uncertainties. As such, the robustness of the combined method and its multiple indicators of performance allow the use of intelligent adaptation for improving the performance of the estimation process. For real-time applications, online neural network adaptation may be used to improve the performance by progressively reducing specific modeling uncertainties in the system. In this paper, a new parameter estimation method that uses concepts associated with the EKF, the VSF, and neural network adaptation is introduced. The performance of this method is considered and discussed for applications that involve parameter estimation such as fault detection. [ABSTRACT FROM AUTHOR]

This manuscript pertains to the application of an inner-loop control strategy to electromechanical flight surface actuation systems. Modular electromechanical actuators (EMAs) are increasingly used in lieu of centralized hydraulics for the control of flight surfaces in the aerospace sector. The presence of what is termed as a dead zone in these actuators significantly affects the maneuverability, stability, and the flight profiles of aircrafts that use this actuation concept. The hypothesis of our research is that flight surface actuation systems may be desensitized to the effects of dead zone by using a control strategy with multiple inner loops. The proposed strategy involves (a) high-gain inner-loop velocity control of the driving motor and (b) inner-loop compensation for the differential velocity between the motor versus the aileron. The above hypothesis is confirmed by theoretical and simulated analyses using the model of an EMA flight surface actuator. Our results indicate that for small input signals, this strategy is very effective and that it can (a) considerably increase the bandwidth and the crossover frequency of the system and (b) considerably improve the time response of the system. Further to this analysis, this manuscript presents guidelines for the design of EMA systems. [ABSTRACT FROM AUTHOR]

The article presents a study investigating the ability of the hydrostatic actuation system in moving heavy loads. As part of the research, the design of a high-precision hydrostatic actuation system referred to as the ElectroHydraulic Actuator is presented. The result showed that hydrostatic actuation systems are able to manipulate heavy loads with submicron precision and a large stroke, after moving a large load of 20 Kg with a precision of 100 nm and a stroke of 12 cm.

In this paper, an Interacting multiple model (IMM) adaptive estimation approach is incorporated to design an optimal adaptive control law for stabilizing an Unmanned vehicle. Due to variations of the forward velocity of the Unmanned vehicle, its aerodynamic derivatives are constantly changing. In order to stabilize the unmanned vehicle and achieve the control objectives for in-flight conditions, one seeks for an adaptive control strategy that can adjust itself to varying flight conditions. In this context, a bank of linear models is used to describe the vehicle dynamics in different operating modes. Each operating mode represents a particular dynamic with a different forward velocity. These models are then used within an IMM filter containing a bank of Kalman filters (KF) in a parallel operating mechanism. To regulate and stabilize the vehicle, a Linear quadratic regulator (LQR) law is designed and implemented for each mode. The IMM structure determines the particular mode based on the stored models and in-flight input-output measurements. The LQR controller also provides a set of controllers; each corresponds to a particular flight mode and minimizes the tracking error. Finally, the ultimate control law is obtained as a weighted summation of all individual controllers whereas weights are obtained using mode probabilities of each operating mode. [ABSTRACT FROM AUTHOR]

A common problem pertaining to linear or nonlinear systems is the design of a combined robust control and estimation strategy that can effectively deal with noise and uncertainties. The variable structure control (VSC) and its special form of sliding mode control (SMC) demonstrate robustness with regard to uncertainties, although their performance can be severely degraded by noise. As such they can benefit from using state estimates obtained from filters. In this regard, this paper considers the use of a recently proposed robust state and parameter estimation strategy referred to as the variable structure filter (VSF) in conjunction with SMC. The contribution of this paper is a new strategy that combines sliding mode control with the variable structure filter. In the presence of bounded parametric uncertainties and noise, this combined method demonstrates robust stability both in terms of control and state estimation. Furthermore, the combined strategy can be used to achieve high regulation rates or short settling time. The combined VSF and SMC strategy is demonstrated by its application to a high precision hydrostatic system, referred to as the electrohydraulic actuator system. [ABSTRACT FROM AUTHOR]

Many autonomous vehicles and advanced driver-assistance systems are equipped with front-facing cameras that detect and track objects using deep-learning-based algorithms. However, the localization capability of monocular cameras is often overlooked. In this paper, a novel method for estimating the pixel-wise error in a detected object's location versus its ground truth is proposed. As the object moves away from the camera, the pixel errors are shown to be normally distributed with unique spreads along the image's vertical axis (y-pixel). The pixel error appears to be smaller as objects get farther away, while at the same distance range, objects have similar error distribution across the camera's horizontal view. The horizontal axis (x-pixel) error appears to be smaller while the distance moves further away. However, the x-pixel location along a constant y-pixel row has no impact on the error distribution. The estimated x and y-pixel error distributions can in turn be used to form a spatial error distribution for finding the location of a detected object within a certain confidence interval. The spatial errors are then projected onto the world coordinate system using a camera transformation matrix to give a more realistic sense of what this error means. The results show that location estimation using monocular cameras generates an elliptical error distribution around the object with a larger error in the y-pixel direction compared to the x-pixel direction. This error distribution can be important to fuse information from multiple range-detecting sensors as well as multi-vehicle and multi-object tracking. The uncertainty characterization for position measurement, as demonstrated in this paper is an essential element of tracking and, is sensor and algorithm dependent. [ABSTRACT FROM AUTHOR]

Model-based state-of-charge (SOC) and state-of-health (SOH) estimation for lithium-ion batteries has been widely applied in electrified vehicles, while the SOC and SOH estimators are highly coupled and nonlinear in conventional techniques. This leads to a bulky design of observer network and complicates the stability analyses. In this article, a new reduced-decoupling SOC and SOH coestimation algorithm based on convex optimization is proposed. This scheme estimates the battery SOC from the battery model and does not require the classic Coulomb-counting method. Therefore, it can decouple the capacity estimation from the SOC estimator and reduce the strong interaction existing in conventional coestimation methods. Besides, all state variables can be solved together by one estimator, which is straightforward and avoids the complicated observer network. Owing to the decoupling design, the stability of the proposed method becomes more intuitive and can be always guaranteed according to the convexity analysis without using other stabilizing approaches. In consequence, a weak-interaction and robust coestimation algorithm of SOC and SOH can be realized by the proposed technique. The experiments on a 5.4-Ah lithium polymer battery are implemented to validate the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

This study investigates the possibility of using dry mechanical mixing techniques to prepare ideal composite powders for L-PBF applications. For this purpose, two AlSi12-reinforced composite powders with 0.5 and 1 wt% graphene were prepared by ball milling and regular mixing processes. The powder attributes (size, size distribution and shape), behaviors (absorptivity, packing density, and flowability), relative humidity, and O/C/H content of the composite powders were quantitatively assessed after mixing and compared to those of monolithic AlSi12. The attachment of the graphene particles and their evolutions during each mixing technique were qualitatively investigated. The quality of the graphene powder before and after mixing was studied by Raman spectroscopy, and the nature of the carbonaceous constituent in the composite powders was identified based on the Raman peak positions, shapes and relative intensities. The results of this study prove that only through ball milling ideal composite powders for the L-PBF applications can be achieved. [Display omitted] • Graphene/AlSi12 composite powders were prepared by ball milling and regular mixing. • Powder attributes, behaviors, humidity, and O/C/H content were assessed. • Quality and nature of carbonaceous constituent was studied by Raman spectroscopy. • Ideal composite powders for PBF applications can only be achieved by ball milling. [ABSTRACT FROM AUTHOR]

This paper introduces a reliable strategy for the state of charge (SOC) and the state of health (SOH) estimation of healthy and aged Lithium polymer cells. Dynamics of the cell are modeled using some equivalent circuit models and parameters of each model are calculated by adaptive particle swarm optimization. The modeling process involves modeling and parametric uncertainties as well as measurement and instrumentation noise. They may degrade the performance of an optimal filter for SOC and SOH estimation. To alleviate effects of such uncertain factors, the smooth variable structure filter (SVSF) is implemented. The SVSF is a novel robust state estimation method that benefits from the robustness property of variable structure systems. The performance of the SVSF is compared with the extended Kalman filter (EKF) for real-time SOC estimation of a healthy and an aged Lithium polymer cell. The paper moreover presents a novel method for SOH estimation using the SVSF’s chattering signal and without the need for modeling the cell undergoes aging. Experiments show performance benefits of the SVSF for reliable SOC and SOH estimation of healthy and aged Lithium polymer cells. [ABSTRACT FROM AUTHOR]

Real-time prediction of the battery's core temperature and terminal voltage is very crucial for an accurate battery management system. In this paper, a combined electrochemical, heat generation, and thermal model is developed for large prismatic cells. The proposed model consists of three sub-models, an electrochemical model, heat generation model, and thermal model which are coupled together in an iterative fashion through physicochemical temperature dependent parameters. The proposed parameterization cycles identify the sub-models’ parameters separately by exciting the battery under isothermal and non-isothermal operating conditions. The proposed combined model structure shows accurate terminal voltage and core temperature prediction at various operating conditions while maintaining a simple mathematical structure, making it ideal for real-time BMS applications. Finally, the model is validated against both isothermal and non-isothermal drive cycles, covering a broad range of C-rates, and temperature ranges [−25 °C to 45 °C]. [ABSTRACT FROM AUTHOR]

A multilayered neural network is a multi-input, multi-output nonlinear system in which network weights can be trained by using parameter estimation algorithms. In this paper, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feed-forward 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 training performance is tested on three benchmark pattern classification problems. Furthermore, a study is presented comparing the popular back-propagation method, the extended Kalman filter, and the SVSF. [ABSTRACT FROM AUTHOR]

In this paper, an engine fault detection and classification technique using vibration data in the crank angle domain is presented. These data are used in conjunction with artificial neural networks (ANNs), which are applied to detect faults in a four-stroke gasoline engine built for experimentation. A comparative study is provided between the popular backpropagation (BP) method, the Levenberg–Marquardt (LM) method, the quasi-Newton (QN) method, the extended Kalman filter (EKF), and the smooth variable structure filter (SVSF). The SVSF is a relatively new estimation strategy, based on the sliding mode concept. It has been formulated to efficiently train ANNs and is consequently referred to as the SVSF-ANN. The accuracy of the proposed method is compared with the standard accuracy of the Kalman-based filters and the popular BP algorithms in an effort to validate the SVSF-ANN performance and application to engine fault detection and classification. The customizable fault diagnostic system is able to detect known engine faults with various degrees of severity, such as defective lash adjuster, piston chirp (PC), and chain tensioner (CT) problems. The technique can be used at any dealership or assembly plant to considerably reduce warranty costs for the company and manufacturer. [ABSTRACT FROM PUBLISHER]

In this paper, a sliding mode control (SMC) strategy is applied to a pulse width modulation (PWM)-driven pneumatic muscle actuator system using high speed on/off solenoid valves. Servo-pneumatic systems with PWM-driven on/off valves can be used instead of expensive servo valves to decrease complexity, weight, and cost of servo-pneumatic systems. Due to the highly nonlinear nature of pneumatics, the system is difficult to model accurately which leads to unmodelled dynamics and uncertainties. In this paper, a robust and nonlinear SMC approach is implemented in order to control the system with sufficient accuracy. A nonlinear model is developed in a single-input single-output form by studying the flow, pressure, and force dynamics of the system. The SMC strategy is applied to three different system configurations: single on/off valve, two on/off valves, and a servo valve. The performance and effectiveness of these configurations are investigated under sinusoidal tracking at different frequencies. The robustness of the controllers is studied by varying the inertia of the system and by applying external disturbances to the system. [ABSTRACT FROM AUTHOR]