State Estimation Research Articles

Complex-Valued Projection Statistics for Hybrid Power System State Estimation

Complex-Valued Projection Statistics for Hybrid Power System State Estimation

Some Results on the Properties of Discrete-Time LTI State-Space Systems

Abstract This work brings forward several interesting facts about system properties of linear time-invariant discrete-time systems like output controllability, state controllability and state observability, trackability, and input and state observability. Particularly, the work brings about the inter-relationships between these properties while taking a state-space formulation-based approach to come up with simple facts that are backed up by proofs using preliminary linear algebra.

Observation of edge states derived from topological helix chains.

Introducing the concept of topology has revolutionized materials classification, leading to the discovery of topological insulators and Dirac-Weyl semimetals1-3. One of the most fundamental theories underpinning topological materials is the Su-Schrieffer-Heeger (SSH) model4,5, which was developed in 1979-decades before the recognition of topological insulators-to describe conducting polymers. Distinct from the vast majority of known topological insulators with two and three dimensions1-3, the SSH model predicts a one-dimensional analogue of topological insulators, which hosts topological bound states at the endpoints of a chain4-8. To establish this unique and pivotal state, it is crucial to identify the low-energy excitations stemming from bound states, but this has remained unknown in solids because of the absence of suitable platforms. Here we report unusual electronic states that support the emergent bound states in elemental tellurium, the single helix of which was recently proposed to realize an extended version of the SSH chain9,10. Using spin- and angle-resolved photoemission spectroscopy with a micro-focused beam, we have shown spin-polarized in-gap states confined to the edges of the (0001) surface. Our densityfunctional theory calculations indicate that these states are attributed to the interacting bound states originating from the one-dimensional array of SSH tellurium chains. Helices in solids offer a promising experimental platform for investigating exotic properties associated with the SSH chain and exploring topological phases through dimensionality control.

Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

Abstract The effect of machine learning and other enhancements on statistical-dynamical forecasts of soil moisture (0-10cm and 0-100cm) and a reference evapotranspiration fraction (Evaporative Stress Index, ESI) on sub-seasonal time scales (15-28 days) are explored. The predictors include the current and past land surface conditions, and dynamical model hindcasts from the Sub-seasonal to Seasonal (S2S) Prediction Project. When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored.

Disturbance Observation and Suppression in an Airborne Electro-Optical Stabilized Platform Based on a Generalized High-Order Extended State Observer.

Active disturbance rejection control (ADRC) is widely used in airborne optoelectronic stabilization platforms due to its minimal reliance on the mathematical model of the controlled object. The extended state observer (ESO) is the core of ADRC, which treats internal parameter variations and external disturbances as total disturbances, observes the disturbances as extended states, and then compensates them into the control loop to eliminate their effects. However, the ESO can only achieve a precise estimation of constant or slowly varying disturbances. When the disturbance is periodically changing, satisfactory results cannot be obtained. In this paper, a generalized high-order extended state observer (GHOESO) is proposed to achieve the precise estimation of known frequency sinusoidal disturbance signals and improve disturbance suppression levels. Through numerical simulations, a traditional ESO and GHOESO are compared in terms of disturbance observation capability and disturbance suppression ability for single and compound disturbances based on our prior knowledge of disturbance frequency. The effectiveness of the proposed GHOESO method is verified. Finally, the algorithm is applied to an airborne optoelectronic stabilization platform for a 1°/1 Hz swing experiment on a space hexapod swing table. The experimental results demonstrate the superiority of the GHOESO proposed in this paper.

A class of adaptive predefined‐time extended state observers for high‐order strict‐feedback systems

AbstractFast state estimation for nonlinear systems has been a key issue in control theory and the recently developed predefined‐time stability acts as an effective tool for the problem. However, the studies of predefined‐time state observer for nonlinear systems are rare. In this article, we consider the predefined‐time extended state observer (PTESO) design for a class of high‐order strict‐feedback systems subject to Hölder continuous nonlinearities and lumped disturbance. By developing an adaptive mechanism and using time‐varying functions, a class of adaptive PTESOs is designed to estimate the unavailable states and the lumped disturbance, in which the convergence time can be tightly and explicitly preset by only one parameter, irrelevant to the initial conditions. The adaptive mechanism is used to improve the transient performance under wide‐range lumped disturbance. Moreover, thanks to the design of the time‐varying gains, the PTESO could realize fast observation with trivial peaking observation errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed observer.

Adaptive consensus control of uncertain nonlinear multi-agent systems with communication delays and relative output information

This paper explores the challenges of distributed adaptive consensus control problems in nonlinear multi-agent systems with unknown parameters and communication delays. To address these issues, a novel backstepping-based adaptive state feedback control protocol is proposed by utilizing a reference dynamic system. Furthermore, an output-feedback consensus control algorithm incorporating a state observer is introduced. Through the application of Laplace transformation, matrix properties, and graph theory, it is demonstrated that the closed-loop multi-agent system ensures the boundedness of all signals and achieves output consensus, even in the presence of nonuniform communication delays. Numerical simulations on a group of inverted pendulums illustrate the effectiveness of these algorithms.

Motion Modeling and State Estimation With Bistatic Range and Doppler Measurements

Motion Modeling and State Estimation With Bistatic Range and Doppler Measurements

Fully Autonomous Brick Pick and Place in Fields by Articulated Aerial Robot: Results in Various Outdoor Environments

Picking and placing of objects by aerial robots in fields is an important and challenging task for a range of outdoor activities, including industry and rescue operations. General strategies depend on the magnetic force to the pick object; however, this approach lacks both generality and robustness. Therefore, we focus on an articulated structure to grasp bricking. Another issue in performing pick-and-place tasks in fields is autonomous recognition using onboard sensors. In this article, we present a fully autonomous pick-and-place scheme in outdoor environments using articulated aerial robots. First, an articulated robot model with an actively tiltable sensor is developed to guarantee robustness in both state estimation and object detection. Second, object detection methods are designed according to the distance between the robot and target object. Third, a comprehensive motion strategy is developed to perform an autonomous object searching, picking, and placing sequence. In particular, a visual servoing method for robot position control is also proposed in this motion strategy to improve the robustness while approaching the target. Finally, we present the experimental results of autonomous brick picking and placing by our proposed methods in various outdoor environments, including a real robotics challenge, the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). To the best of our knowledge, our study represents the first successful attempt at fully autonomous object grasping by an articulated aerial robot in a field.

Double-Observer-Based Bumpless Transfer Control of Switched Positive Systems

This paper investigates the bumpless transfer control of linear switched positive systems based on state and disturbance observers. First, state and disturbance observers are designed for linear switched positive systems to estimate the state and the disturbance. By combining the designed state observer, the disturbance observer, and the output, a new controller is constructed for the systems. All gain matrices are described in the form of linear programming. By using co-positive Lyapunov functions, the positivity and stability of the closed-loop system can be ensured. In order to achieve the bumpless transfer property, some additional sufficient conditions are imposed on the control conditions. The novelties of this paper lie in that (i) a novel framework is presented for positive disturbance observer, (ii) double observers are constructed for linear switched positive systems, and (iii) a bumpless transfer controller is proposed in terms of linear programming. Finally, two examples are given to illustrate the effectiveness of the proposed results.

Dynamic Compensation of Distorted Measured Forces Using Improved Luenberger Observer

Dynamic Compensation of Distorted Measured Forces Using Improved Luenberger Observer

State Estimation for Delayed Memristive Neural Networks With Multichannel Round-Robin Protocol and Multimodal Injection Attacks

State Estimation for Delayed Memristive Neural Networks With Multichannel Round-Robin Protocol and Multimodal Injection Attacks

Toward User-Centered Bounded State Estimation in Power Grids: Theory and Method

Toward User-Centered Bounded State Estimation in Power Grids: Theory and Method

Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented.

Distributed State Estimation for Mixed Delays System Over Sensor Networks With Multichannel Random Attacks and Markov Switching Topology.

This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given H∞ antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.

Characterization of Candidate Solutions for X-Ray Pulsar Navigation

X-ray pulsar based navigation (XNAV) can be used to estimate spacecraft state information based on observation of distant pulsars. XNAV system concepts typically require an initial position estimate since there are many candidate positions which may produce the same measurement. If position ambiguity can be resolved, XNAV may enable full state estimation without prior information. This study seeks to efficiently find candidate spacecraft positions and determine how to select pulsars to minimize the number of candidate solutions within a given domain. A numeric scheme for determining candidate positions is developed for an arbitrary number of observed pulsars. Pulsar selection criteria are developed in terms of relative direction, period, and phase accuracy. Results indicate that it is more time efficient to observe additional pulsars rather than improve the accuracy of pulsar measurements. A smaller angular separation and increased period of the observed pulsars reduces the number of candidate solutions within a given domain. Phase measurement accuracy should be improved simultaneously for all pulsars rather than prioritizing a single pulsar. These selection criteria are verified by evaluating the number of candidate solutions for permutations of 34 real pulsars.

Onboard State Estimation Around Didymos With Recurrent Neural Networks and Segmentation Maps

Onboard State Estimation Around Didymos With Recurrent Neural Networks and Segmentation Maps

Hybrid Transmission Scheduling Protocol for State Estimation Over a Constrained Channel

Hybrid Transmission Scheduling Protocol for State Estimation Over a Constrained Channel

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

Active Damping for LC-Filtered PMSM Based on Low-Pass Filter Capacitor Current Feedback and Extended State Observer

Active Damping for <i>LC</i>-Filtered PMSM Based on Low-Pass Filter Capacitor Current Feedback and Extended State Observer