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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]

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. [DOI: 10.1115/1.2907393] Keywords: Parameter estimation, Variable structure systems, Kalman filter

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. [DOI: 10.1115/1.2936382]

In this paper reports on an important finding, that is, hydrostatic actuation systems are able to manipulate heavy loads with submicron precision and a large stroke. In this relation, the design of a high-precision hydrostatic actuation system referred to as the ElectroHydraulic Actuator (EHA) is presented. A laboratory prototype of this system has achieved an unprecedented level of performance by being able to move a large load of 20 Kg with a precision of 100 nm and a stroke of 12 cm. This level of performance places the hydrostatic actuation concept in competition with piezoelectric platforms in terms of positional accuracy. Experimental results from this prototype are reported and analyzed. [DOI: 10.1115/1.2229259]

In this paper, a new method for state estimation, referred to as the variable structure filter (VSF), is briefly reviewed. The VSF method is model based and has been formulated for its application to linear systems. It provides a means of explicitly defining the level of uncertainty in the dynamic model used by the filter, thus allowing for tradeoff between the performance indicators of the filter. This trade-off feature is not generally explicitly available in some of the more established concepts used for state estimation. In this paper, an extension to the VSF method for its application to nonlinear systems is proposed and referred to as the extended variable structure filter (EVSF). The derivation of EVSF and its application to a nonlinear robotic example is provided. [DOI: 10.1115/1.2194070]

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]

Autonomous vehicles have been emerging over the past few years because of the sophisticated processing of data from different types of perception sensors, such as LiDAR, radar, and camera. Ground segmentation plays an important role in the sequence of data processing for environment perception tasks, as it can help to reduce the size of data to be processed and further decrease the overall computational time. However, the over-segmentation, under-segmentation, or slow-segmentation on non-flat surface usually occurs due to the characteristics of the 3D LIDAR data, such as occlusion in complex urban environment. To address these problems, in this paper, we proposed a probability occupancy grid-based approach for real-time ground segmentation by using a single LiDAR sensor. The effectiveness and robustness of our proposed method are evaluated and demonstrated by the real-time experiments that span different traffic scenarios from heavy traffic to light traffic. [ABSTRACT FROM AUTHOR]

Autonomous vehicles need a real-time traffic tracking system in order to interact with multiple moving vehicles in urban situations. Most existing works rely on camera sensors to interpret the surrounding environment. However, camera sensors degrade in conditions of lighting, shadows, and extreme weathers such that they can hardly detect objects. The light detection and ranging (LiDAR) sensors promise to be a good substitute, as they enable highly precise and robust localization across a wide range of conditions. This paper presents a new LiDAR-based online multiple maneuvering vehicle tracking problem and proposes a novel online multi-model smooth variable structure filter to address the problem. The real-time experiments show that our method is able to deliver superior performance compared to other conventional methods. [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]

Traditionally, series electric vehicles, such as bus, construction machine like bulldozer, carry a certain capacity of power batteries, capacitors or other energy storage units. However, these energy storage units not only occupy a few large of vehicle space leading to be uncomfortable for passengers and driver, but also increase production cost, and reduce the powerline's reliability. In order to make the powerline system more compact and reliable, a new series electric system without energy storage unit is proposed in this paper. This new powerline's mathematic models are established. And a fuzzy PI closed-loop control strategy with parametric self-tuning function control is put forward to stabilize the powerline DC-link's voltage fluctuation. The models and control strategies are simulated in Matlab/Simulink software environment. The results give a verification on the new powerline's configuration, as well as the proposed control strategy. [ABSTRACT FROM AUTHOR]

Autonomous self-drive requires intelligence and cognition that relies on observations and tracking of the state of motion of surrounding vehicles. This information can be acquired by using sensors; however, these are often affected by clutter and noise that, in turn, introduce the issues of estimation and data origin uncertainty into the tracking system. The most popular methods for estimation and tracking are based on the well-studied Kalman filter (KF). KF is optimal when noise is white and remains so despite uncertainties in the filter model; the robustness and stability of the KF is affected if this condition is not met. The smooth variable structure filter (SVSF) is a relatively new method that is more robust to disturbances and uncertainties. The SVSF ensures stability by using a discontinuous corrective term that maintains estimates to within a subspace of the true state trajectory. The discontinuous corrective term results in chattering that is removed by using a smoothing boundary layer. In this paper, a generalized covariance formulation of the SVSF and a generalized optimal time-varying smoothing boundary layer are proposed. The generalized optimal SVSF is then combined with a joint probabilistic data association technique for target tracking. The robustness and accuracy of the new form of filtering and data association is validated and comparatively analyzed by its application to an experimental traffic monitoring system based on Light Detection and Ranging. [ABSTRACT FROM PUBLISHER]

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]

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]

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]