Estimation Of Current State Research Articles

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.

PPP based on factor graph optimization

Abstract Kalman or Kalman-related filtering methods are routinely applied in precise point positioning (PPP). However, in robot simultaneous localization and mapping (SLAM) systems, the factor graph optimization (FGO) has proved advantages over filtering methods in recent years, e.g., reducing the linearization errors and support of plug-and-play feature for multiple sensor fusion. Therefore, it would be interesting to apply the FGO to PPP. In addition, it will also facilitate the tight integration of PPP with Visual/LiDAR SLAM. In this work, PPP is solved under the factor graph optimization framework. A factor graph for PPP has been constructed. Results from 268 IGS-MGEX stations show that the factor graph optimization method can achieve a similar performance with that of Kalman filtering. First, the positioning accuracy in the convergence period can be improved for PPP based on factor graph optimization because it optimizes the entire state variables based on all the available observations. For applications that do not require real-time processing, the observation after the current states, e.g., future observations, can also be used to enhance the current state estimation. Second, the accuracy of static PPP is almost the same for the two methods with millimeter-accuracy for horizontal directions and centimeter-accuracy for vertical directions. Third, the kinematic PPP for both methods can achieve centimeter-level accuracy in horizontal directions and decimeter-level accuracy in vertical directions. Although the performance is comparable, it is noted that the computational efficiency of factor graph optimization method is still a problem. For each epoch, the average of elapsed time for Kalman filtering is 132 microseconds, while that of factor graph optimization method is 9664 microseconds. The elapsed time of factor graph optimization method can be further improved if the fix-window optimization technique is applied, which will be investigated in the future.

Verification of the MLP network-based current sensor fault classifier for vector-controlled AC motor drives

In modern drive systems, the aim is to ensure their operational safety. Damage can occur not only to the components of the motor itself but also to the power electronic devices included in the frequency converter and the sensors in the measurement circuit. Critical damage to the electric drive that makes its further exploitation impossible can be prevented by using fault-tolerant control (FTC) algorithms. These algorithms are very often combined with diagnostic methods that assess the degree and type of damage. In this paper, a fault classification algorithm using an artificial neural network (ANN) is analysed for stator phase current sensors in AC motor drives. The authors confirm that the investigated classification algorithm works equally well on two different AC motors without the need for significant modifications, such as retraining the neural network when transferring the algorithm to another object. The method uses a stator current estimator to replace faulty sensor measurements in a vector control structure. The measured and estimated currents are then subjected to a classification process using a multilayer perceptron (MLP), which has the advantage of a small structure size compared to deep learning structures. The uniqueness of the method lies in the use of data in the training set that are not dependent on the parameters of a specific motor. Four types of current sensor faults were studied, namely total signal loss, gain error, offset, and signal saturation. Simulations were performed in a MATLAB/SIMULINK environment for drive systems with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). The results show that the algorithm correctly evaluates the type of damage in more than 99.6% of cases regardless of the type of motor. Therefore, the results presented here may help to develop universal diagnostic methods that will work on a wide variety of motors.

Real-time and non-contact estimation of state of charge for lithium-ion battery using laser ultrasonics

Lithium-ion batteries (LIBs) are widely used in portable electronics, electric vehicles, and stationary storage systems due to their high power and high energy. Monitoring the status of LIBs, particularly the state of charge (SOC), is crucial to ensuring battery performance and safety. Non-destructive ultrasonic approaches are recently emerging as a promising method for estimating the battery state and improving safety. However, current battery state estimation methods require contact and the use of a couplant, limiting their practicality and real-time applications. This study presents a non-contact laser ultrasonic system for monitoring the battery state during charge/discharge cycles. The experimental setup utilizes a line laser source to generate ultrasonic waves and an interferometer to detect the wave signal in a pitch-catch mode. Various ultrasonic features, including time-of-flight (TOF) and signal amplitude (SA), are analyzed in the time and frequency domains, respectively, at different degradation rates. The results show significant changes in the ultrasonic features in response to battery SOC and cycling degradation. The proposed non-contact ultrasonic method has the capability to determine the internal status of LIBs, enabling real-time monitoring of battery safety.

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

In electric drive systems, one of the most common faults is related to measurement equipment, including current sensors (CSs). As information about the stator current is crucial to ensure precise control of AC drives, such a fault significantly affects the quality and security of the entire system. For this reason, a modified extended Kalman filter (EKF) has been presented in this paper as an algorithmic solution to restore stator current in the event of CS failure. In order to minimize the impact of rotor and stator resistance variations on the quality of the estimation, the proposed model includes an estimation of the general coefficient of their changes. In contrast to solutions known in the literature, the presented model considers changes in both resistances in the form of a single coefficient. This approach allows us to maintain a low order of the estimator (fifth) and thus minimize the tendency to system instability and decrease computation burden. Extensive simulation tests have shown a significant improvement in the accuracy of stator current estimation under both motor and regenerating modes, a wide speed range (1–100%), and changes in motor parameters.

Rao-Blackwellized particle smoothing for simultaneous localization and mapping

Abstract Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic interpretation of the SLAM task allows for incorporating prior knowledge and for operation under uncertainty. Contrary to the common practice of computing point estimates of the system states, we capture the full posterior density through approximate Bayesian inference. This dynamic learning task falls under state estimation, where the state-of-the-art is in sequential Monte Carlo methods that tackle the forward filtering problem. In this paper, we introduce a framework for probabilistic SLAM using particle smoothing that does not only incorporate observed data in current state estimates, but it also backtracks the updated knowledge to correct for past drift and ambiguities in both the map and in the states. Our solution can efficiently handle both dense and sparse map representations by Rao-Blackwellization of conditionally linear and conditionally linearized models. We show through simulations and real-world experiments how the principles apply to radio (Bluetooth low-energy/Wi-Fi), magnetic field, and visual SLAM. The proposed solution is general, efficient, and works well under confounding noise.

A novel automatic generation control method with hybrid sampling for multi-area interconnected girds

Introduction: The emerging “net-zero carbon” police will accelerate the large-scale penetration of renewable energies in the power grid, which would bring strong random disturbances due to the unpredictable power output. It would affect the coordinated control performance of the distributed grids.Method: From the quadratic frequency modulation perspective, this paper proposes a fast Q-learning-based automatic generation control (AGC) algorithm, which combines full sampling with full expectation for multi-area coordination. A parameter σ is used to balance the state between the full sampling update and only the expectation update so as to improve the convergence accuracy. Meanwhile, fast Q-learning is incorporated by replacing the historical estimation function with the current state estimation function to accelerate the convergence speed.Results: Simulations on the IEEE two-region load frequency control model and Hubei power grid model in China have been performed to validate that the proposed algorithm can achieve optimal multi-area coordination and improve the control performance of frequency deviations caused by the strong random disturbances.Discussion: The proposed Q-learning-based AGC method outperforms the convergence accuracy, speed, and control performance compared with other reinforcement learning algorithms.

Detectability in Discrete Event Systems Using Unbounded Petri Nets

This paper investigated the verification of detectability for discrete event systems based on a class of partially observed unbounded Petri nets. In an unbounded net system, all transitions and partial places are assumed to be unobservable. The system administrator can only observe a few observable places, i.e., the number of tokens at these places can be observed, allowing for the estimation of current and subsequent states. The concepts of quasi-observable transitions, truly unobservable transitions, and partial markings are used to construct a basis coverability graph. According to this graph, four sufficient and necessary conditions of detectability are proposed. Correspondingly, a specific example is proposed to prove that the detectability can be verified in the unbounded net system. Furthermore, based on the conclusion of detectability, the system’s ability to detect critical states was explored by using the basis coverability graph, called C-detectability. Two real-world examples are proposed to show that the detectability of discrete event systems has not only pioneered new research methods, but also demonstrated that the real conditions faced by this method are more general, and it has overcome the limitations of relying only on the ideal conditions of bounded systems for verification.

Multi-rate Kalman filtering for structural dynamic response reconstruction by fusing multi-type sensor data with different sampling frequencies

In this paper, a novel dynamic response reconstruction method based on multi-rate Kalman filtering (MRKF) is presented. The proposed method starts with representing the structural system by the state-space equation. Then, different observation equations are defined, and that selection is based on the availability of sensor types at a specific time. Not only can the multi-type sensor data sampled at different rates be fused directly, but the presented method also relaxes the collocated monitoring requirement. In addition, future observations are used to benefit the current state estimation by the Rauch, Tung, and Striebel smoothing procedure. The unobserved structural dynamic responses are estimated using the MRKF virtual sensing technique with multi-rate sensor data. Several demonstrative numerical tests are performed to verify the superiority and robustness of the presented MRKF method on one benchmark shear frame model. The experimental test employed a computer-vision-based displacement tracking technique. Results show that the proposed method surmounts the obstacle to deploying consumer-grade cameras in structural health monitoring applications, which provide a low-cost sensing solution without sacrificing response estimation accuracies.

Maximum correntropy criterion variational Bayesian adaptive Kalman filter based on strong tracking with unknown noise covariances

The performance of the current state estimation will degrade in the existence of slow-varying noise statistics. To solve the aforementioned issues, an improved strong tracking maximum correntropy criterion variational-Bayesian adaptive Kalman filter is presented in this paper. First of all, the inverse-Wishart distribution, as the conjugate-prior, is adopted to model the unknown and time-varying measurement and process noise covariances, then the noise covariances and system state are estimated via the variational Bayesian method. Secondly, the multiple fading-factors are obtained and evaluated to modify the prediction error covariance matrix to address the problems associated with inaccurate error estimation. Finally, the maximum correntropy criterion is employed to correct the filtering gain, which improves the filtering performance of the proposed algorithm. Simulation results show that the proposed filter exhibits better accuracy and convergence performance compared to other existing algorithms.

Estimating the Spread of Wildland Fires via Evidence-Based Information Fusion

This article presents a new evidential reasoning approach for estimating the state of an evolving wildfire in real time. Given assumed terrain information and localized wind velocity distribution parameters, a probabilistic representation (i.e., the belief state) of a wildfire is forecast across the spatiotemporal domain through a compilation of fire spread simulations. The forecast is updated through information fusion based on observations provided by: 1) embedded temperature sensors and 2) mobile vision agents that are advantageously directed toward locations of information extraction based on the current state estimate. This combination of uncertain sources is performed under the evidence-based Dempster’s rule of combination and is then used to enact sensor reconfiguration based on the updated estimate. This research finds that the evidential belief combination vastly outperforms the standard forecasting approach (where no sensor data are incorporated) in the presence of imprecise environmental parameters.

Active Current Sensor Fault-Tolerant Control of Induction Motor Drive with Online Neural Network-Based Rotor and Stator Resistance Estimation

Abstract This article presents an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CS.

Revisiting State Estimation and Weak Detectability of Discrete-Event Systems

In this paper, we revisit state estimation and weak detectability verification for discrete event systems (DES) from a span-new perspective. Specifically, using the semi-tensor product (STP) technique, we construct two new matrix-based information structures called a current-state estimator (C-estimator) and an initial-state estimator (I-estimator) for computing three fundamental types of state estimates, namely, current-state estimate (CSE), initial-state estimate (ISE), and delayed-state estimate (DSE). The complexity of building C-estimator and I-estimator is polynomial time with respect to the size of a plant. A notion of weak delayed detectability is introduced, which captures that, after observing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_{1}$ </tex-math></inline-formula> -length sequence/string, whether or not one can always accurately determine the state of a plant at this moment after at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_{2}$ </tex-math></inline-formula> steps of delays for some trajectories. Further, using the proposed C-estimator and I-estimator, we discuss the different types of detectability verification problems, including, but not restricted to, weak current-state detectability (C-detectability), weak initial-state detectability (I-detectability), and weak delayed detectability. Accordingly, several necessary and sufficient criteria are derived for verifying the aforementioned different types of detectability. Our approaches are numerically tractable and only involve some basic matrix manipulations. Finally, some examples are given to illustrate the obtained results. Note to Practitioners—State estimation is one of the most fundamental problems in many practical engineering systems. For instance, one needs to infer the state of a manufacturing system before a failure occurs. For a communication system, can we guarantee that whether important information remains secret to outsiders for security requirements? Finding an alternative and efficient approach to capture the state of a plant based on imperfect observations is still crucial for engineers. To solve these problems, in this paper we develop a novel methodology to tackle simultaneously three fundamental categories of state estimation for practical engineering systems that are inherently abstracted as partially-observed discrete-event systems. Our approaches are technically quite different from the existing ones. The novel results obtained in this paper are all of matrix-based characterization, which can be implemented algorithmically by means of the user-friendly STP software package. We believe that the alternative methodology provides an innovative insight for engineers in the field of automatic control.

Current-state opacity verification in discrete event systems using an observer net

Due to the proliferation of contemporary computer-integrated systems and communication networks, there is more concern than ever regarding privacy, given the potential for sensitive data exploitation. A recent cyber-security research trend is to focus on security principles and develop the foundations for designing safety-critical systems. In this work, we investigated the problem of verifying current-state opacity in discrete event systems using labeled Petri nets. A system is current-state opaque provided that the current-state estimate cannot be revealed as a subset of secret states. We introduced a new sub-model of the system, named an observer net. The observer net have the same structure as the plant, but it is distinguished by the use of colored markers as well as simultaneous and recursive transition enabling and firing, which offer an efficient state estimation. We considered two settings of the proposed approach: an on-line setting, in which a current-state opacity algorithm is proposed. The algorithm waits for the occurrence of an observable event and determines if the current observation of a plant reveals the secret behaviour, as well as, an off-line setting, where the verification problem is solved based on a state estimator called a colored estimator. In this context, necessary and sufficient conditions for verifying opacity are developed with illustrative examples to demonstrate the presented approach.

Influence of Parameter Uncertainty to Stator Current Reconstruction Using Modified Luenberger Observer for Current Sensor Fault-Tolerant Induction Motor Drive.

In modern systems with induction motors (IM), in addition to precision, it is also very important to ensure the highest possible reliability and safety. To ensure the above, information about the stator current value is required. If the current sensor (CS) fails, a redundant sensor or an algorithmic solution can be used. The Luenberger observer (LO) can be used to estimate the lost stator current without increasing the cost of the drive system. However, this solution is based on the mathematical model of IM, which is sensitive to its parameters. Therefore, this paper presents a modified LO (MLO) and investigates the effect of a coefficient in the error gain matrix on improving robustness to changes in the IM parameters. As shown by extensive studies, the proposed solution has significantly reduced the influence of the IM parameters on the accuracy of the stator current estimation, which has not been previously reported in the known literature.

A Novel Federated Structure of Invariant EKF Using Left-/Right-Invariant Observations

In this article, we propose a novel federated structure of the invariant extended Kalman filter (IEKF) using the left- and right-invariant observations. A proper invariant error should be chosen to exploit the trajectory-independent property of the IEKF. For the left- and right-invariant observations, the left-IEKF (LIEKF) and the right-IEKF (RIEKF) are designed, respectively. However, in applications, both left- and right-invariant observations are often used together while using multiple aiding sensors to improve estimation performance. If either the LIEKF or RIEKF is selected and implemented as a centralized filter, the measurement matrix contains current state estimates in mismatched observation-related terms. As a result, as the state estimation error increases, the calculation of the Kalman gain is affected, and the filter cannot properly correct the error. For such situations, we propose a federated structure that updates the left- and right-invariant observations separately in the allocated subfilters. In the subfilter assigned with left-invariant observations, the error is corrected using the LIEKF, and in the subfilter assigned with right-invariant observations, the error is corrected using the RIEKF. Since each subfilter uses trajectory-independent Kalman gain, it is expected to improve the estimation performance compared to the centralized structure. The performance of the proposed method is evaluated and compared to the centralized LIEKF and RIEKF through Monte-Carlo simulations for an inertial navigation example. Also, the proposed method is validated by a publicly available dataset of a ground vehicle equipped with a global positioning system (GPS) and wheel encoders.

Spin state evolution of (99942) Apophis during its 2029 Earth encounter

We explore the effects of the 2029 Earth encounter on asteroid (99942) Apophis’ non-principal axis spin state, leveraging refined orbit, spin state, and inertia information provided by more recent optical and radar observations. Propagating the asteroids’ coupled orbit and rigid body attitude dynamics through the flyby, we present the range of possible post-flyby spin states. These spin state distributions will be valuable for planning Apophis observation campaigns and spacecraft missions, most notably OSIRIS-APEX. The simulations indicate that gravitationally induced changes to the asteroid’s tumbling periods and rotational angular momentum direction (pole) will likely be significant and measurable. For the current spin state and inertia estimates and their uncertainties, Apophis is likely to remain in a short axis mode (SAM) tumbling state but its effective spin rate could halve or double. Its pole is likely to shift by 10 degrees or more and increase in longitude while moving closer to the ecliptic plane. These spin state changes are very sensitive to the asteroid’s close approach attitude and mass distribution. With ground-based tracking of the asteroid’s spin state through the encounter, this sensitivity will help refine mass distribution knowledge. We also discuss the implications of this abrupt spin state alteration for Apophis’ Yarkovsky acceleration and geophysical properties, identifying possible pathways for surface and internal changes, most notably if Apophis is a contact binary. Comparison of the pre and post-flyby inertia estimates obtained from the ground-based observations will help assess the extent of possible geophysical changes.

Bayesian Integration in a Spiking Neural System for Sensorimotor Control.

The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks.

Current Sensorless Control for a Wound Rotor Synchronous Machine Based on Flux Linkage Model

Reducing manufacturing costs and increasing reliability are two major issues for the automotive industry. The development of sensorless control methods and estimation techniques for synchronous machines are ways to reduce the impact of these constraints. In this article, a control method is applied to a wound rotor synchronous machine (WRSM), often used in HEV applications, to estimate stator currents and control the machine without using any current sensor on the stator side. In this estimator, instead of the electric state model based on the currents, a flux-based model is used, which increases the precision of the estimator, improves the dynamics of stator currents, and has a better behavior in the case of magnetic saturation. Using this model and applying a Luenberger observer, the stator currents are estimated and then used in the current control loop. The performances of the proposed method are studied in a simulation part. Experimental tests are also performed to verify the effectiveness of the proposed control method.