Initial Error Research Articles

The modeling and analysis of operating characteristics and temperature drift of deflector jet servo valve under the wide temperature range

The Deflector Jet Servo Valve (DJSV) holds a crucial role within the electro-hydraulic servo system, as its performance directly influences the equipment reliability. DJSV operates within a wide temperature range, and the temperature fluctuations result in the magnetomotive force variations of the permanent magnets and changes in the working air gaps, which influence the operating characteristics of the torque motor and contribute to temperature drift. Consequently, a theoretical model is proposed to comprehensively analyze the impact of these factors on the operating characteristics and temperature drift of the DJSV. The results indicate that as the temperature increases, the magnetic flux of working air gap decreases, while the temperature drift increases. Additionally, among the five initial air gap errors discussed, a centrosymmetric error of less than 0.02 l can significantly reduce the temperature drift. The proposed modeling and analysis method of temperature drift can be used to guide the design and analysis of DJSV in wide temperature range.

Inverse estimation of heat flux and temperature field for a nonlinear heat transfer system using step-renewed two-stage Kalman filter

Inverse estimation of the thermal input and states is a useful tool for the monitoring of a thermal system. Nonlinear heat transfer is a quite common issue in this kind of problem. As the thermal properties are temperature-dependent, it is difficult to inversely estimate the input and temperature field. To deal with this problem, a step-renewed scheme is adopted into the weighted optimal two-stage Kalman filter (WOTSKF), forming step-renewed WOTSKF (SR-WOTSKF). This strategy is a marching method, without solving the Jacobian matrix in extended Kalman filter. The feasibility of dealing with the nonlinear inverse heat transfer problem is implemented by comparing WOTSKF and SR-WOTSKF. Then, the weighting factor, initial guess of the input heat flux and measurement noise are changed to investigate their effects on the simultaneous estimation results of heat flux and temperature field. It is found that when the weighting factor is 0.95, the relative error of the estimated heat flux using SR-WOTSKF is approximately 2.73% which is much lower than that using WOTSKF. Results using SR-WOTSKF demonstrate that the estimation results are independent of the initial guess and the estimated heat flux can track the exact one in a short period. An incorrect initial guess causes the initial estimation error of the temperature and heat flux and it makes no sense to following period. When the standard deviations of the measurement noise are adopted as 0.01, 0.10, 0.50 and 1.00, the mean relative error of the heat flux is 2.73%, 4.79%, 7.71% and 9.43%, demonstrating the strong robustness of SR-WOTSKF to resist the measurement noise.

Improvement of Traction Force Estimation in Cornering through Neural Network

<div>Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels. The second network utilized longitudinal slip ratios of the driving wheels and longitudinal and lateral accelerations of the vehicle as inputs. The training of the neural networks was performed using data from straight-line accelerations, circuit maneuvers, and a sinus steering maneuver. Both neural networks were designed as multi-output networks capable of simultaneously estimating longitudinal force errors for both driving wheels. The estimator was tested by making two laps on the Hockenheim circuit in the opposite direction. The initial root mean square error (RMSE) was substantially reduced using corrective neural networks. These findings affirm the effectiveness of the neural network-based approach in improving traction force estimation under combined slip conditions, overcoming the limitations of the Pacejka formula in cases of non-pure slip, thereby paving new avenues for the implementation of more advanced and secure vehicle control systems.</div>

Tunnel Prescribed Control of Nonlinear Systems With Unknown Control Directions.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

Receding Horizon ₂-FIR Filtering for Uncertain Disturbed Systems With Initial and Data Errors

Receding Horizon ₂-FIR Filtering for Uncertain Disturbed Systems With Initial and Data Errors<i/>

Adaptive neural DSC for nonlinear switched systems with prescribed performance and input saturation

This paper solves the problem of an adaptive neural dynamic surface control for a class of uncertain strict-feedback nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function (RBF) neural networks (NNs) are used to handle unknown nonlinear functions, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model and the dynamic surface control (DSC) technique is used to overcome the problem of 'explosion of complexity' inherent in the control design. At last, by using the common Lyapunov function method in combination with the backstepping technology, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. Simulation studies demonstrate the effectiveness of the proposed method.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection.

This article proposes a real-time neural network (NN) stochastic filter-based controller on the Lie group of the special orthogonal group [Formula: see text] as a novel approach to the attitude tracking problem. The introduced solution consists of two parts: a filter and a controller. First, an adaptive NN-based stochastic filter is proposed, which estimates attitude components and dynamics using measurements supplied by onboard sensors directly. The filter design accounts for measurement uncertainties inherent to the attitude dynamics, namely, unknown bias and noise corrupting angular velocity measurements. The closed-loop signals of the proposed NN-based stochastic filter have been shown to be semiglobally uniformly ultimately bounded (SGUUB). Second, a novel control law on [Formula: see text] coupled with the proposed estimator is presented. The control law addresses unknown disturbances. In addition, the closed-loop signals of the proposed filter-based controller have been shown to be SGUUB. The proposed approach offers robust tracking performance by supplying the required control signal given data extracted from low-cost inertial measurement units. While the filter-based controller is presented in continuous form, the discrete implementation is also presented. In addition, the unit-quaternion form of the proposed approach is given. The effectiveness and robustness of the proposed filter-based controller are demonstrated using its discrete form and considering low sampling rate, high initialization error, high level of measurement uncertainties, and unknown disturbances.

Prescribed-time prescribed performance control for stochastic nonlinear input-delay systems with arbitrary bounded initial error

The issue of adaptive prescribed-time prescribed performance control (PTPPC) for stochastic nonlinear input-delay systems with arbitrary bounded initial error is discussed in this paper. With the help of prescribed-time prescribed performance function (PTPPF) and a novel switching model-based error transformation function, the proposed method can ensure that the tracking error reaches the specified accuracy within an arbitrary prescribed time, and another significant advantage is that it removes the restriction that the initial error must be within the constrained boundary of existing prescribed performance control (PPC) methods. In addition, an auxiliary system is introduced to manipulate the input delay, which eliminates the limitations of computational complexity and small delays in the Padé approximation method. By combining backstepping with neural network approximation technology, a novel controller is raised, under which the tracking accuracy and convergence time can be pledged, and the whole signals of closed-loop system are bounded in probability. Simulation experiments testify the effectiveness of the control approach.

Electrochemical Model-Based State-Space Approach for Real-Time Parameter Estimation of Lithium-Ion Batteries

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage systems, due to their high energy density, long cycle life, and low self-discharge rate. Accurate estimation of battery state of charge (SOC) and state of health (SOH) is crucial for their safe and efficient operation.In this study, we propose a model-based state-space solver for real-time monitoring of lithium-ion batteries under various operating conditions. The state-space model simplifies a single-particle model and enables simulations considering the current state of the battery using a Kalman filter. The model includes a one-dimensional Single Particle Model with Electrolyte (SPMe) and uses eigenfunction expansion to solve the mass conservation equation for the electrolyte phase. The final form of the state-space model for the solid and electrolyte phase includes a voltage equation using lithium-ion concentration. The proposed solver was validated by comparing it with the SPMe solver, and the results agree well.To achieve on-line updating of the battery model parameters, we propose a multi-scale parameter adaptive method based on dual Kalman filters for estimating the SPMe model parameters. The fast time-varying parameters are separated from the slowly time-varying parameters, and then each group is estimated by a different Kalman filter with a different time constant. This method enables the estimation of both the SOC and all parameters, including the SPMe model parameters. Additionally, we provide a parameter adjustment method for dual extended Kalman filters when estimating multiple parameters.The proposed method reduces the influence of the initial state error on the algorithm and improves its robustness. Experimental results demonstrate that the accuracy of the algorithm is improved by separating the fast and slow parameters and estimating them with different Kalman filters. The proposed solver can be used with Kalman filters and the like in an onboard environment due to its fast calculation speed. Figure 1

The initial errors occurring over Pacific-Atlantic Oceans and exerting large disturbing effect on ENSO predictability

Using a novel data analysis method for predictability dynamics, the impacts of initial sea surface temperature (SST) errors over Pacific and Atlantic Oceans on the predictability of eastern and central Pacific El Niño (i.e., EP and CP El Niño) are investigated. The results reveal the initial SST errors that cause large disturbing effects on EP and CP El Niño forecasting, respectively. These initial errors are both exhibiting a positive-negative-positive-negative-positive chain structure along the direction from northwest to southeast over the Pacific for EP and CP El Niño, which resemble a combined mode of North Pacific Victoria Mode (VM), eastern tropical Pacific positive SST pattern (ETPPSP), and South Pacific meridional mode (SPMM); simultaneously, they exhibit a positive-negative meridional dipole pattern over South Atlantic, referred to as South Atlantic subtropical dipole mode (SASD); additionally, there exist initial warm SST anomalies in the equatorial Atlantic for EP El Niño and in the north tropical Atlantic for the CP El Niño. The above initial errors lead to the underestimation of both CP and EP El Niño. Further analyses illustrate that the initial warm SST errors in the north tropical Atlantic are positively correlated with the VM-like error pattern, which competed with the effect of the ETPPSP, makes the intensity of CP El Niño underestimated; whereas the SASD-like error pattern is revealed to have a positive relationship with the SPMM-like error mode, which only exists during EP El Niño period and interacts with the ETPPSP for much weak EP El Niño intensity. It is obvious that, for predicting which type of El Niño will occur, attention should also be paid to the initial sea temperature accuracy in the Atlantic Ocean under the interference effect of the Pacific Ocean temperature uncertainties.

A GPS multipath mitigation method in coordinate-domain considering the effects of gross errors and missing data

Multipath is a significant obstacle to achieving high-precision deformation monitoring using the Global Positioning System (GPS) technique. Sidereal filtering (SF) can mitigate GPS multipath effects based on the multipath models derived from historical measurements, but its practical application in complex scenarios is still challenging due to unavoidable gross errors and missing data problems. In this paper, an improved SF algorithm is proposed that accurately extracts multipath immune to the effects of gross errors and missing values in historical measurements, aiming to obtain accurate displacements in a harsh environment. Specifically, a gross error detection method is first developed based on the dynamic time warping (DTW) algorithm to filter gross errors. Then, we adopt the multichannel singular spectrum analysis (MSSA) algorithm to extract initial multipath errors. Finally, we propose a weighted fusion (WF) algorithm to eliminate the impact of missing values and obtain the final multipath sequences. Extensive experiments with datasets collected from a real-world landslide, including both stable and sliding stations, demonstrate the effectiveness of our proposed method. Results show that compared with the traditional SF method, our proposed method can effectively mitigate the effects of gross errors and missing values on multipath modeling, with the multipath filtering accuracy improving by approximately 12% and 21% for stable and sliding scenarios, respectively.

Observer‐based distributed average tracking of second‐order multi‐agent systems with disturbances

AbstractThis article addresses the problem of distributed average tracking for second‐order multi‐agent systems with disturbances. The unknown disturbances inject into the velocity dynamics, resulting in the degradation of system stability. An extended state observer is designed to estimate the velocities and disturbances. Then, an observer‐based average tracking protocol is proposed, which does not require velocity measurements, and the unknown disturbances are compensated by the estimations. The protocol is implemented in a distributed fashion, without requiring any global information, and is robust to initialization errors. Through Lyapunov analysis, it is verified that the second‐order multi‐agent system can achieve distributed average tracking under the proposed protocol. Finally, the effectiveness of the control method is demonstrated by simulations.

Event-triggered dynamic prescribed performance control for oxygen stoichiometry and stack temperature in PEM fuel cell systems with actuators faults

Oxygen stoichiometry (OS) and stack temperature (ST) significantly affect the output performance and durability of proton exchange membrane fuel cells. This paper investigates the prescribed performance tracking control strategy of OS and ST under external disturbances, modeling errors, actuators faults and limited communication resources. The total disturbances are compensated by a variable-bandwidth extended state observer to simplify the control design and enhance the system robustness. To tolerate excessive disturbances and relax the strict requirement on the initial conditions, a new event-triggered adaptive sliding mode controller that integrates modified dynamic performance constraints and nonsingular error transformation is developed to ensure the user-designed transient and steady-state performance constraints, while saving the communication costs and avoiding the chattering and jump in control signals. The numerical validation results under combined working scenarios indicate that the proposed controller always ensures the preset error constraints and possesses superior performance in tracking the optimal OS value 2 and the optimal ST value 353.15K than other three representative controllers, with the integral absolute errors of 0.732 for OS tracking and 12.94 for ST tracking; besides, it achieves satisfactory robustness to uncertain model parameters and relatively large initial tracking errors.

State of charge estimation of lithium-titanate battery based on multi-model extended Kalman filter considering temperature and current rate

To tackle the issue of accurately estimating the state of charge (SOC) of lithium-titanate (Li-Ti) batteries in complex vehicle applications, a multi-model extended Kalman filter (MM-EKF) algorithm considering the effects of temperature and current rate is proposed. Based on the operational characteristics of Li-Ti batteries in the context of electric vehicle applications, second-order RC equivalent circuit models (ECMs) are established to account for the temperature and current rate influences. Model parameters are identified using an adaptive recursive least squares method with a forgetting factor based on experimental data. Subsequently, a SOC estimation method based on the MM-EKF algorithm for Li-Ti batteries is proposed and its effectiveness is validated under different ambient temperatures. Experimental results demonstrate that the MM-EKF algorithm, which considers the effects of temperature and current rate, can accurately estimate the SOC of Li-Ti batteries. The maximum estimation error is within 5 % at different ambient temperatures, and the algorithm can quickly eliminate initial SOC errors. Consequently, it fulfills the requirements for SOC estimation of hybrid tracked vehicles in intricate operating conditions.

Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre

Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

Event-Triggered Tracking Control for a Class of Nonlinear Systems: A Dynamic Gain Approach

This note presents a dynamic gain approach to the event-triggered tracking control for a class of strict-feedback nonlinear systems. A novel shifting function is designed to make the initial tracking error arbitrarily bounded, which significantly relaxes the requirement on the initial tracking error. To achieve the objective that the tracking error does not exceed the constraint after the pre-specified finite time, a barrier Lyapunov function and a new reduced-order observer are constructed by using the shifting function and the desired tracking signal. Based on the proposed dynamic gain approach, an event-triggered output feedback tracking controller is designed. It is proved that the developed control scheme guarantees that all the closed-loop signals are bounded and the tracking error constraint is strictly obeyed after a specified finite time. Compared with the celebrated backstepping method, the dynamic gain approach does not involve any virtual control variables, which avoids the complicated iterative procedure. The effectiveness of the proposed approach is verified by an example.

Acceptability and Requirements for Digital Interactive Television Systems in Geriatric Institutions

AbstractBackgroundInformation and communication technologies have great potential to address the psychosocial needs of Older Adults (OAs) living in geriatric institutions. With the development of Digital Interactive Television (DiTV) systems, communication, entertainment, online purchases and other functionalities and services are accessible directly on a television screen. Thus, DiTV could be a promising and accessible solution to help meet several needs of OAs, especially in geriatric settings.MethodA mixed‐methods research was conducted to identify barriers of DiTV use among geriatric residents with the aim of identifying accessible specifications for the system, and recommendations for its implementation. First, semi‐structured interviews were conducted within 3 French nursing homes already equipped with a DiTV (OAs: n = 18; professionals: n = 6). Interviews were then transcribed with the MAXQDA software and processed using a thematic deductive analysis inspired by a multidimensional health technology assessment model. Secondly, user tests compared the ability to use a DiTV (e.g., errors made and need of assistance) by OAs living in geriatric institutions (n = 20), and OAs living at home (n = 15). Four questionnaires (e.g., socio‐demographic and technological habits questionnaire, System usability Scale (SUS), and Technology Assessment Model (TAM)) helped identify the acceptability and usability of the DiTV.ResultPreliminary results showed that DiTV systems can be well accepted by OAs who recognize the advantage of this system in terms of communication and entertainment. However, its use may be hindered by organizational (e.g., workload), technological (e.g., ergonomic issues), human (e.g., health issues), ethical (e.g., privacy), and safety factors (e.g., frustration due to technical problems). The second part of the study revealed that OAs living in geriatric institutions made more initiation errors and needed more assistance (help or confirmation) in comparison to OAs living at home, even though the DiTV provides some guidance on the remote‐control buttons.ConclusionBeyond organizational, safety, and technological issues, the presentation and training on the DiTV are essential to reassure and accompany OAs in the discovery of a new device, even one as familiar as the television.

Acceptability and Requirements for Digital Interactive Television Systems in Geriatric Institutions

AbstractBackgroundInformation and communication technologies have great potential to address the psychosocial needs of Older Adults (OAs) living in geriatric institutions. With the development of Digital Interactive Television (DiTV) systems, communication, entertainment, online purchases and other functionalities and services are accessible directly on a television screen. Thus, DiTV could be a promising and accessible solution to help meet several needs of OAs, especially in geriatric settings.MethodA mixed‐methods research was conducted to identify barriers of DiTV use among geriatric residents with the aim of identifying accessible specifications for the system, and recommendations for its implementation. First, semi‐structured interviews were conducted within 3 French nursing homes already equipped with a DiTV (OAs: n = 18; professionals: n = 6). Interviews were then transcribed with the MAXQDA software and processed using a thematic deductive analysis inspired by a multidimensional health technology assessment model. Secondly, user tests compared the ability to use a DiTV (e.g., errors made and need of assistance) by OAs living in geriatric institutions (n = 20), and OAs living at home (n = 15). Four questionnaires (e.g., socio‐demographic and technological habits questionnaire, System usability Scale (SUS), and Technology Assessment Model (TAM)) helped identify the acceptability and usability of the DiTV.ResultPreliminary results showed that DiTV systems can be well accepted by OAs who recognize the advantage of this system in terms of communication and entertainment. However, its use may be hindered by organizational (e.g., workload), technological (e.g., ergonomic issues), human (e.g., health issues), ethical (e.g., privacy), and safety factors (e.g., frustration due to technical problems). The second part of the study revealed that OAs living in geriatric institutions made more initiation errors and needed more assistance (help or confirmation) in comparison to OAs living at home, even though the DiTV provides some guidance on the remote‐control buttons.ConclusionBeyond organizational, safety, and technological issues, the presentation and training on the DiTV are essential to reassure and accompany OAs in the discovery of a new device, even one as familiar as the television.

Output Feedback MPC for Nonlinear System in Large Operation Range

This paper studies the output feedback model predictive control (MPC) being suitable to a large operation range of the constrained system represented by a nonlinear model with a bounded disturbance. The model is further parameterized as a multiple linear parameter varying (LPV) state-space equation, each LPV representation being valid around an exclusive equilibrium. Off-line, for each equilibrium, a set of local control laws (in the dynamic output feedback), each having its region of attraction, are calculated. All the control laws for all the equilibriums constitute a lookup table, and all the corresponding regions of attraction take a union. On-line, the real-time control law is searched as one, as per its region of attraction, in the lookup table when the estimated system state moves inside of the union. Once the initial estimation error lies in a pre-specified set, the augmented state is guaranteed to converge to the neighborhood of the target equilibrium, and the constraints on input and output are consistently satisfied. An example is given to illustrate the effectiveness of the proposed algorithm.