Sensor Delay Research Articles

Vehicle modeling and state estimation for autonomous driving in terrain

The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented.The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model’s applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain.

Hybrid-attack-resistant distributed state estimation for nonlinear complex networks with random coupling strength and sensor delays

In this paper, a recursive distributed hybrid-attack-resistant state estimation (SE) scheme is proposed for a class of time-varying nonlinear complex networks (NCNs) subject to random coupling strength (RCS) and random sensor delays (RSDs) under hybrid attacks. A hybrid-attack model is considered to characterize the random occurrence of denial-of-service (DoS) attacks and deception attacks. The objective of the problem to be solved is to develop a recursive distributed estimation method such that, in the presence of RCS, RSDs and hybrid attacks, a locally optimized upper bound (UB) on the estimation error covariance (EEC) is ensured. By employing the mathematical induction method, a UB is firstly derived on the EEC. Subsequently, the obtained UB is minimized by appropriately designing the estimator gain (EG). Furthermore, a sufficient criterion guaranteeing the exponential boundedness (EB) of SE error is elaborated in the mean square sense (MSS). Finally, simulation experiments with localization applications of multiple mobile indoor robots are conducted to illustrate the applicability of the proposed SE scheme.

Output feedback linearization control for nanorobots with actuator and sensor delays

This paper proposes an output predictive control design for the endovascular magnetic resonance imaging (MRI)-guided nanorobots in the presence of actuator and sensor delays. The nanorobot is modeled considering the effects of hydrodynamic drag force, apparent weight, and magnetic motive force. Using feedback linearization approach, the state feedback control is designed. To physically implement the predicted states in the state feedback control, the high-gain predictor is constructed. The performance recovery property of the output feedback using the high-gain predictor is achieved. The numerical simulation demonstrates the effectiveness of the designed controllers and the performance recovery property of the designed high-gain predictor.

Data-driven sensor delay estimation in industrial processes using multivariate projection methods

A key step in preparing industrial data for multivariate statistical modelling of (batch-)continuous processes is the estimation of sensor delays along the production line, to use as a correction for understanding relationships between them. Without such a correction, the measurements collected from the sensors do not all relate to the same portion of material travelling through the plant, which is where from a physical point of view the correlations are expected to arise from. Most methods to do these corrections currently reported in literature are limited in that they optimize only correlations bivariately between the different sensors, despite the eventual goal of being understood as a multivariate process model. This work proposes several methods for multivariate sensor delay estimation, based on multivariate projection methods, and critically investigates these using simulated and empirical process data from two industrial facilities. The objective is to provide more clarity on if, how, and why taking into account these multivariate correlations using such projection methods is beneficial. One of the proposed methods, named PLS-SEQ, was shown to outperform bivariate sensor delay estimation, which is regarded as benchmark method in this work, for all datasets. The average reduction in delay estimation error achieved by using PLS-SEQ is between 6% and 64%. The other newly proposed multivariate projection-based methods did not show such unanimous improvement in accuracy, but helped identifying the presence of sensor clusters in industrial data as a novel key challenge for sensor delay estimation. The presented results illustrate that projection-based multivariate methods, in particular the newly proposed method PLS-SEQ, show promising potential for sensor delay estimation, outperforming the bivariate sensor delay estimation methods used as benchmark. Ultimately, using these methods can improve the predictive and descriptive quality of statistical models for key industrial processes.

Finite‐dimensional, output‐predictor‐based, adaptive observer for heat PDEs with sensor delay

SummaryWe are considering the problem of designing observers for heat partial differential equations (PDEs) that are subject to sensor delay and parameter uncertainty. In order to get finite‐dimensional observers, described by ordinary differential equations (ODE), we develop a design method based on the modal decomposition approach. The approach is extended so that both parameter uncertainty and sensor delay effects are compensated for. To cope more effectively with sensor delay, an output predictor is designed and the online provided output predictions are substituted to the future output values in the observer. To compensate for parameter uncertainty, we design a parameter estimator providing online parameter estimates, which are substituted to the unknown parameters in the observer. The parameter estimator design is made decoupled from the observer gain design by using an appropriate decoupling transformation. Using an analysis of the small‐gain‐theorem type, the whole (state and parameter) estimation error system is shown to be exponentially stable, under well‐defined conditions on the observer dimension, the sensor delay, and signal persistent excitation (PE).

Three-model-driven fault diagnosis method for complex hydraulic control system: Subsea blowout preventer system as a case study

Hydraulic control is a control pattern that uses compression fluid as the energy medium and information medium. Hydraulic system is widely used in the control of industrial systems because of its flexibility and reliability. Hydraulic systems have the characteristics of strong fault concealment, significant sensor delay, and a complex signal transmission mechanism. Hence, it is very difficult to identify the faults of systems under the influence of powerful nonlinear time-varying characteristic. The diagnosis of complex hydraulic control system is a problem facing the current research. In order to cope with the challenges caused by limited sensors and concealment of faults, a three-model-driven fault diagnosis method is proposed for complex hydraulic control system. The three hydraulic control system models with regard to energy, fluid and information are proposed and established to explain the work process of the system from different perspectives. The diagnostic model is completed by establishing a fault reasoning model based on the Bayesian network. A redundant control system for subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy.

Adaptive Observer Design for Heat PDEs with Sensor Delay and Parameter Uncertainties

Adaptive Observer Design for Heat PDEs with Sensor Delay and Parameter Uncertainties

Energy Efficient Clustering Mechanism for Malicious Sensor Nodes in IOT Based MWSN

A mobile wireless sensor network (MWSN) is a collection of wireless nodes that may be set up anywhere and at any time without the need for a pre-existing network structure. Network performance is challenging factor due to nature of mobility and misbehaving nodes. Data loss and misbehaving in sensor nodes degrades performance of network. Some circumstances some malevolent sensor nodes predisposed to eradicate capability of network. The goal of this study is to use an irregular set approach to distinguish hostile nodes. Broadcasting information in route entry table identifies malicious node. Each sensor node in network preserves entry table and broadcast record regarding nearby nodes. To estimate broadcasting record metrics are premeditated as data deliverance proportion, throughput, delay, packet drop, fault rate. Mobile nodes with diverse velocity are simulated in NS2 environment which contains variable node clusters. Based broadcasting records of mobile nodes with diverse velocity are considered to create information table. Fine and malicious nodes are recognized based on rules derived in table of irregular set tactic. Shortest path without malicious node is elected to broadcast packets in the environment. Proposed tactic consequences reveal irregular set tactic amplifies network capability data deliverance percentage, throughput and dwindled end-to-end delay in mobile sensors.

Sliding mode control for disturbance rejection and estimation under measurement delay with PDE‐backstepping predictor

AbstractIn this article, we address the disturbance rejection problem for linear plants with delayed measured output. The output delay is allowed to be time‐varying and of arbitrary duration. The disturbances that may represent actuator faults are generated by an exogenous dynamic system and can be matched or unmatched. A predictor–based observer is designed via the PDE–backstepping (Partial Differential Equation–backstepping) method so that an estimate of the current state vector, as well as that of unmeasured disturbance, can be obtained. These estimates are used in to regulate the output and reject the disturbance by output feedback sliding mode control. In addition, a solution for finite‐time disturbance rejection (rather than exponential only) is also introduced when the sensor delay is constant. Numerical examples illustrate the performance and robustness of the both proposed predictor‐feedback control systems.

A phase-time-path-difference approach for online wave direction and wave number estimation from measured ship motions in zero and forward speed using a single inertial measurement unit

This study investigates the potential capability of a relatively new and unexplored signal-based approach for shipboard wave estimation. The approach uses the phase-time-path-differences (PTPDs) from an array of shipboard sensors to uniquely resolve the wave propagation direction and wave number. We derive a kinematic PTPD model accounting for forward vessel speed and assess its theoretical foundation to model the sensor delays on a rigid body. The forward-speed PTPD model is structurally equivalent to the zero-speed model considered in previous works, thus retaining the same observability results provided by a noncollinear array of a minimum of three sensors. Moreover, based on the outlined theory and PTPD model, we propose a methodology to estimate the main wave propagation direction and wave number online by employing a fast Fourier transform (FFT), an unscented Kalman filter (UKF), and a rigid-body measurement transformation based on a single inertial measurement unit (IMU). Provided that the vessel in question can be considered a rigid body, a single IMU is sufficient to obtain the desired wave quantities instead of three IMUs, as initially proposed in our previous work. Additionally, our methodology incorporates a novel frequency threshold to avoid distorted wave components caused by the effect of vessel filtering. The performance of our PTPD method is evaluated on data collected from a wave tank and full-scale experiments involving a vessel with zero and non-zero forward speed. The results show very good agreement with the reference wave values reported from a commercial wave radar and wave buoys operating in proximity to the vessel, indicating that our proposed method is competitive with existing wave measurement technology in terms of accuracy and online performance while being cheap, easy to install, flexible, and robust against environmental influences.

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models.

New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities. The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon. As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively. Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

Insights into the Fusion Correction Algorithm for On-Board NOx Sensor Measurement Results from Heavy-Duty Diesel Vehicles

Over the last decade, Nitrogen Oxide (NOx) emissions have garnered significantly greater attention due to the worldwide emphasis on sustainable development strategies. In response to the issues of dynamic measurement delay and low measurement accuracy in the NOx sensors of heavy-duty diesel vehicles, a novel Multilayer Perceptron (MLP)–Random Forest Regression (RFR) fusion algorithm was proposed and explored in this research. The algorithm could help perform post-correction processing on the measurement results of diesel vehicle NOx sensors, thereby improving the reliability of the measurement results. The results show that the measurement errors of the On-board Nitrogen oxide Sensors (OBNS) were reduced significantly after the MLP-RFR fusion algorithm was corrected. Within the concentration range of 0–90 ppm, the absolute measurement error of the sensor was reduced to ±4 ppm, representing a decrease of 73.3%. Within the 91–1000 ppm concentration range, the relative measurement error was optimised from 35% to 17%, providing a reliable solution to improve the accuracy of the OBNS. The findings of this research make a substantial contribution towards enhancing the efficacy of the remote monitoring of emissions from heavy-duty diesel vehicles.

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis

A new state observer for active disturbance rejection control with measurement noise and output delay using the PDE-backstepping predictor

This paper designs a new observer to address the issue of disturbance rejection in nonlinear plants with sense delay and measurement noise. The effect of sensor delay is characterized as a transport equation, and a predictor-based extended state observer has been designed using the PDE (partial differential equation) backstepping method. A cascaded ESO (extended state observer) architecture has been introduced to further reduce the limitation of measurement noise on observer bandwidth. This new cascade observer is capable of rapidly and accurately reconstructing system signals and compensating for output delay while avoiding the amplification of measurement noise. The numerical example has demonstrated the efficacy of the proposed observer structure, and extensive comparative experimental results have further confirmed its superiority.

Extended dissipative filter design for discrete-time interconnected fuzzy systems with time-varying delays subject to cyber attacks

This article addresses the problem of extended dissipative filtering design for the class of nonlinear interconnected systems with time-varying delays under the cyber attacks. The dynamics of proposed interconnected systems (ISs) is modeled by the Takagi-Sugeno fuzzy (TSF) IF-THEN rules. The relevant practical model is used to describe the network connectivity between the plant and the filter. In addition, to develop the filter for nonlinear systems in more practical sense, the sensor delays and the influence of cyber attacks are taken into account during the signal transmission. In order to verify that the developed closed-loop system is asymptotically stable with extended dissipative, sufficient conditions are derived by constructing the delay-dependent Lyapunov-Krasovskii functional (LKF) and employing the summation inequalities through the linear matrix inequalities (LMIs) approach. The outcomes of theoretical results can be used to analyze the various performances like H∞ performance, (Υ1,Υ2,R)−α−dissipativity, passivity, mixed H∞/passivity, and l2−l∞ performance in a single framework. At last, a numerical example is presented to demonstrate the proposed method and to validate the theoretical findings in a practical sense.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

Frequency-Domain Estimation of a Transfer Matrix of an Uncommon Quadrotor in Hover

In this article, system identification of an uncommon quadrotor in hover is discussed. Two counter-rotating big rotors on the longitudinal axis and two counter-rotating small tilt rotors on the lateral axis form this quadrotor configuration. First, the nonlinear dynamic model of this vehicle is derived from Newton–Euler formulation. Next, using the approximate linear hover model and the suitable rotor mixing matrix for axes decoupling, simplified attitude dynamics of this quadrotor are obtained. However, the effects of sensor delays, flexible modes of the airframe, and inexact decoupling are not visible in this diagonal model, which is mainly based on physical principles. Therefore, a system identification method is used to obtain a more accurate model, which is required for control design. Hence, during parametric identification of nominal model coprime factors, a small robust control criterion is targeted. Then, the frequency response function of the proposed quadrotor prototype in hover is obtained. Next, the linear parametric model of the vehicle is estimated by solving optimally conditioned least squares and subsequent Gauss–Newton problems. After that, using validation-based uncertainty quantification, the uncertain model set is constructed around the estimated coprime factors. The resulting model set and its robust control relevance are depicted.

Robust Attitude Controller Design for an Uncommon Quadrotor With Big and Small Tilt Rotors

AbstractIn this article, a robust attitude controller design for an uncommon quadrotor aerial vehicle is discussed. This aerial vehicle is designed to have two big rotors on the longitudinal axis to increase the lift capacity and flight endurance, and two small tilt rotors on the lateral axis to stabilize the attitude. Similar to other multirotors, linearization of the full nonlinear model and using an appropriate rotor mixing matrix give the approximate diagonal attitude model of this quadrotor around hover. However, this ideal model lacks sensor delays, uncertain parameters, flexible modes of a structure, and inexact decoupling dynamics. Therefore, using this model in the control design limits the achievable attitude control performance. Unlike most studies, a system identification method is applied to estimate a more accurate model and increase the resulting attitude control performance. The aim of this paper is to obtain a suitable nominal model with accompanying uncertainty using robust control criterion in the system identification. In this way, an uncertain model that gives high performance in the subsequent robust control design is obtained. The experimental results show that this combined identification and robust control procedure improves attitude control performance compared to existing classical controller design methods.

Secured healthcare monitoring for remote patient using energy-efficient IoT sensors

The Internet of Things (IoT) and cloud computing are found to be effective in providing on-time medical support for patients by storing and accessing their clinical data on servers. There are some issues, such as communication delay, security, and energy loss in IoT sensors, that affect the quality of the service. In this research, a remote patient monitoring system using energy-efficient IoT sensors is proposed to meet security demands. The optimization of network clustering and cluster head selection is performed based on trust, inter- and intra-cluster distance, and energy level by the proposed SS-ROW algorithm, which is formulated by integrating the characteristics of the squirrel and sparrow with Ciphertext Key 2 Policy (CK2P) encryption on data. Further, an effective trust mechanism based on CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and KP-ABE (Key-Policy Attribute-Based Encryption) is established to attain better results in terms of the number of alive nodes, energy left, and throughput.

Filter and sensor delay synchronization in incremental flight control laws

This paper introduces and investigates several methods of filtering and delay synchronization in incremental control law structures. It is shown that proper filter and delay synchronization is of great importance for both the performance and stability of the control laws. It is derived how to synthesize complementary filters in case of sensor dynamics, sensor delays and internal control law filtering such as roll-off filters and notch filters. The complementary filters are compared to common synchronization approaches previously applied in incremental control laws. It is shown that the proposed methods are beneficial for Multiple Input Multiple Output (MIMO) systems in case that different delays and filtering is present in the different measurement signals. In case of strongly coupled input effectiveness, the synchronization on the actuator feedback may become even unstable, where the complementary filter approach recovers the design closed-loop behavior. In addition, it is derived how to initialize the complementary filters for a transient-free engagement in non-steady-state conditions. The results are compared in simulation with a roll rate control law for a fixed wing aircraft.