Stochastic optimisation in Riemannian manifolds, especially the Riemannian stochastic gradient method, has attracted much recent attention. The present work applies stochastic optimisation to the task of recursive estimation of a statistical paramete...
This study presents a robust estimation approach for linear discrete-time systems subject to parametric uncertainties. To address model mismatch, the proposed method enhances the MHE framework, thereby improving estimation accuracy. Based on this fra...
Modal parameter estimation is crucial in vibration-based damage detection and deserves increased attention and investigation. Concrete arch dams are prone to damage during severe seismic events, leading to alterations in their structural dynamic char...
In this paper, the recursive form of an optimal finite impulse response filter is proposed for discrete time-varying state-space models. The recursive form of the finite impulse response filter is derived by employing finite horizon Kalman filtering...
This paper presents a computationally efficient state-of-health (SOH) estimator that is readily applicable to automotive battery management systems (BMSs). The proposed scheme uses a recursive estimator to improve the original scheme based on a batch...
This paper deals with the recursive state estimation issue for mobile robot localization under a dynamic event-based mechanism. To enhance the utilization of communication resources, a dynamic event-based transmission protocol is utilized to reduce u...
This paper considers the Recursive Kernel Estimator (RKE) of the expectile-based conditional shortfall. The estimator is constructed under a functional structure based on the ergodicity assumption. More preciously, we assume that the input-variable i...
In this paper, we propose a non-parametric estimator of the conditional hazard function weighted on the recursive kernel method given an explanatory variable taking values in a semi-metric space when the scalar response is censored. Under the ergodic...
The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise...
State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will ch...
The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined meth...
The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speec...
For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery managem...
With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. S...
Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and...
The model predictive control (MPC) technique is widely employed in process industries as a control scheme. The quality of the model used greatly influences the performance of the MPC. In time-varying systems, the plant model plays a critical role in...
This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission...
While system identification methods have developed rapidly, modeling the process of batch polymerization reactors still poses challenges. Therefore, designing an intelligent modeling approach for these reactors is important. This paper focuses on ide...
This paper investigates the problem of robust fusion state estimation for multi-sensor systems under the influence of time-correlated fading channels, incorporating a dynamic event-triggered mechanism (DETM). The randomly occurring parameter uncertai...
In this paper, we investigate the recursive
State-of-charge (SOC) estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive...
Wind power systems often introduce interfering DC components that distort power measurements and threaten grid stability. To address these issues, this paper proposes a novel delayed sampling-based grid parameter estimation method that explicitly acc...
A countershaft brake is used as a transmission brake (TB) to realize synchronous shifting by reducing the automated mechanical transmission (AMT) input shaft’s speed rapidly. This process is performed to reduce shifting time and improve shifting qual...
The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and o...
Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production. Real-time operator support and performance monitoring require accurate info...
This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control...
Precise time synchronization is indispensable for enabling seamless coordination in Intelligent Transportation Systems (ITS) which rely on reliable vehicle communications. This work introduces lightweight online clock skew compensation algorithms bas...
Accurately estimating a sequence of latent variables in state observation models remains a challenging problem, particularly when maintaining coherence among consecutive estimates. While forward filtering and smoothing methods provide coherent margin...
In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the a...
In this article, we study the problem of the recursive estimator of the expectile regression of a scalar variable Y given a random variable X that belongs in functional space. We construct a new estimator and study the asymptotic properties over a ge...
This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-sq...
Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filt...
This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading...
Kalman filtering is a linear quadratic estimation (LQE) algorithm that uses a time series of observed data to produce estimations of unknown variables. The Kalman filter (KF) concept is widely used in applied mathematics and signal processing. In thi...
The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is stu...
The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and i...
Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but m...
This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sen...
A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorith...
State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint...
Attitude update rate is one of the important indicators of star sensor performance. In order to resolve the problem of the low attitude update rate of star sensors, this paper proposes a star sensor attitude update method based on star point correcti...
To address the persistent computational bottlenecks in point cloud registration, this paper proposes a hierarchical grouping strategy named HiGoReg. This method incrementally updates the pose of the source point cloud via a hierarchical mechanism, wh...
The motion control of autonomous underwater vehicles (AUVs) is affected by waves near the ocean surface or in shallow-water areas. Therefore, to counteract the influence of waves, we need to remove them by designing a filter. The wave peak frequency...
This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize informa...
An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in thi...
This paper presents a technique to localize a stereo camera for in-crash toeboard deformation measurement. The proposed technique designed a sensor suite to install not only the stereo camera but also initial measurement units (IMUs) and a camera for...
A condition-based monitoring (CBM) system provides the possibility for the railway industry to guarantee reliability by executing prompt and low-cost maintenance. In this study, a simple model-based condition monitoring strategy for the railway vehic...
Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) wit...
Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. T...
The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-t...
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