State Estimation Error Research Articles

Error State Estimation of Localization Using 3D Point Cloud According to the Surrounding Environment

Error State Estimation of Localization Using 3D Point Cloud According to the Surrounding Environment

Efficient State-Estimation of Uncertain Max-Plus Linear Systems with High Observation Noise

This paper presents a new approach to bounded error state-estimation for uncertain max-plus linear systems. This method yields the smallest interval vector including the real state in a guaranteed way. The parameters of the max-plus linear systems are assumed to be bounded, the nondeterministic measurement of the system output is assumed to be given as well as the interval vector including the state at the preceding step. The observation matrix is assumed to be with high noise, i.e., the width of its interval components is large enough. The computation makes intensive use of the residuation theory (Baccelli et al., 1992) over the intervals. This method is worth of interest because it gives a guaranteed over-approximation of the support of the state and could be used to improve the probabilistic approach considered in the literature with an overall complexity of computation lower than existing methods.

How vulnerable is innovation-based remote state estimation: Fundamental limits under linear attacks

This paper is concerned with the problem of how secure the innovation-based remote state estimation can be under linear attacks. A linear time-invariant system equipped with a smart sensor is studied. A metric based on Kullback–Leibler divergence is adopted to characterize the stealthiness of the attack. The adversary aims to maximize the state estimation error covariance while stay stealthy. The maximal performance degradations that an adversary can achieve with any linear first-order false-data injection attack under strict stealthiness for vector systems and ε-stealthiness for scalar systems are characterized. We also provide an explicit attack strategy that achieves this bound and compare this attack strategy with strategies previously proposed in the literature. Finally, some numerical examples are given to illustrate the results.

Noisy multimodal brain image registration using markov random field model

In this paper, a new scheme is proposed to register brain Magnetic Resonance (MR) and Computed Tomography (CT) images under a noisy environment. The scheme is developed based on the notion of mutual information and this is extended to the feature space-based registration. The 2D joint histogram of the noisy MR and CT images has been computed and this is assumed to be the degraded version of the true 2D joint histogram that corresponds to the CT and MR images. The true 2D joint histogram is modeled as Markov Random Field (MRF) model and is estimated from the degraded one by formulating the problem in Maximum A Posterior (MAP) estimation framework. The MAP estimates are obtained by the combination of Simulated Annealing (SA) and Iterated Conditioned Mode (ICM) algorithms. The Mutual Information (MI) at different values of the parameter vector θ are computed from the estimated joint histogram and is maximized to determine the optimal registration parameter. The MI curve is modeled as multivariate Gaussian distribution and the registration parameter vector is obtained with a confidence interval. The scheme has been tested with the slices of patients from the Retrospective Image Registration Evaluation (RIRE) database. The efficacy of the scheme on MR and CT volumes is demonstrated by rendering the registered slices. The proposed algorithm is successfully tested with different degrees of Gaussian, Rician and Rayleigh noise conditions. Performance measures such as parameter estimation errors, error statistics and landmark based validation are used to demonstrate the efficacy of the proposed algorithm. The scheme is found to exhibit improved performance, especially at high noisy conditions as compared to the existing schemes.

Charge-Based Capacitive Self-Sensing With Continuous State Observation for Resonant Electrostatic MEMS Mirrors

This contribution presents charge-based capacitive self-sensing with a continuous full state observer for a parametrically driven resonant electrostatic 1D MEMS mirror considering precise and seamless estimation. Based on current integrators, series capacitances or a capacitance network the direct charge self-sensing principles are investigated and compared considering leakage currents, precision and a minimum implementation effort. In comparison to the other charge sensing methods, the proposed methods directly measure the charge changes while the drive voltage is switched on. Since resonant MEMS mirrors are driven by a rectangular signal, the direct self-sensing implies a lack of data when the drive voltage is switched off. A nonlinear observer is also proposed to estimate the full mirror state continuously based on an identified MEMS mirror model. The capacitive charge self-sensing methods achieve overall a high sensing precision of less than 0.14 % RMSE and the observer estimation error of the full state is below 1 % peak-to-peak error regardless of the availability of the charge self-sensing measurements, demonstrates accurate continuous full state estimation. [2021-0130]

Simultaneous Estimation of the State, Unknown Input, and Output Disturbance in Discrete-Time Linear Systems

A state observer and unknown input and output disturbance estimators are proposed for discrete-time linear systems corrupted by bounded unknown inputs and output disturbances. Necessary and sufficient conditions for the existence of the state observer and disturbance estimators are given. Relationships with the strong observer of Hautus are investigated. The state, unknown input, and output disturbance estimation errors are guaranteed to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{\infty }$</tex-math></inline-formula> -stable with prescribed performance level. The design of the state observer and disturbance estimators are given in terms of linear matrix inequalities. The proposed estimators can be applied to detect adversarial attacks on the communication channels between the controller and actuators and between the plant sensors and the controller.

Collaborative Allocation and Optimization of Path Planning for Static and Mobile Sensors in Hybrid Sensor Networks for Environment Monitoring and Anomaly Search.

In this study, a novel collaborative method is developed to optimize hybrid sensor networks (HSN) for environmental monitoring and anomaly search tasks. A weighted Gaussian coverage method hs been designed for static sensor allocation, and the Active Monitoring and Anomaly Search System method is adapted to mobile sensor path planning. To validate the network performance, a simulation environment has been developed for fire search and detection with dynamic temperature field and non-uniform fire probability distribution. The performance metrics adopted are the detection time lag, source localization uncertainty, and state estimation error. Computational experiments are conducted to evaluate the performance of HSNs. The results demonstrate that the optimal collaborative deployment strategy allocates static sensors at high-risk locations and directs mobile sensors to patrol the remaining low-risk areas. The results also identify the conditions under which HSNs significantly outperform either only static or only mobile sensor networks in terms of the monitoring performance metrics.

Attack-resilient state estimation with intermittent data authentication

Network-based attacks on control systems may alter sensor data delivered to the controller, effectively causing degradation in control performance. As a result, having access to accurate state estimates, even in the presence of attacks on sensor measurements, is of critical importance. In this work, we analyze performance of resilient state estimators (RSEs) when any subset of sensors may be compromised by a stealthy attacker. Specifically, we consider systems with the well-known l0-based RSE and two commonly used sound intrusion detectors (IDs). For linear time-invariant plants with bounded noise, we define the notion of perfect attackability (PA) when attacks may result in unbounded estimation errors while remaining undetected by the employed ID (i.e., stealthy). We derive necessary and sufficient PA conditions, showing that a system can be perfectly attackable even if the plant is stable. While PA can be prevented with the use the standard cryptographic mechanisms (e.g., message authentication) that ensure data integrity under network-based attacks, their continuous use imposes significant communication and computational overhead. Consequently, we also study the impact that even intermittent use of data authentication has on RSE performance guarantees in the presence of stealthy attacks. We show that if messages from some of the sensors are even intermittently authenticated, stealthy attacks could not result in unbounded state estimation errors.

An enhanced UAV safety control scheme against attacks on desired trajectory

The detection and compensation of attacks are crucial for UAV safety. This paper focuses on the case where the desired trajectory sent by ground control station (GCS) is attacked. Based on the analysis of the attack transmission mechanism, an integrated scheme of attack detection and compensation is presented. The attack detection mechanism is established based on the trajectory tracking error and state estimation error. In addition, the proposed scheme can not only detect attacks but also distinguish attacks from unknown abrupt disturbances. In the proposed scheme, attack observer (AO) and learning observer (LO) are developed to address the coupling problem of additive and multiplicative attacks. Finally, the effectiveness of the proposed scheme is validated by both simulation and experimental tests.

Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm

Aiming at the state estimation error caused by inaccurate battery model parameter estimation, a model-based state of charge (SOC) estimation method of lithium-ion battery is proposed. This method is derived from parameter identification using an adaptive genetic algorithm (AGA) and state estimation using fractional-order unscented Kalman filter (FOUKF). First, the fractional-order model is proposed to simulate the characteristics of lithium-ion batteries. Second, to tackle the problem of fixed values of probabilities of crossover and mutation in the genetic algorithm (GA) in model parameter identification, an AGA has been proposed. Then, the FOUKF method is used to assess battery SOC. For the data redundancy problem caused by the fractional-order algorithm, a time window is set to enhance the computational efficiency of the fractional-order operator. Finally, the experimental results show that the developed AGA-FOUKF algorithm can increase the correctness of SOC estimation.

Estimation of False Data Injection Attacks for Load Frequency Control Systems

False data injection attacks (FDIAs) against the load frequency control (LFC) system can lead to unstable operation of power systems. In this paper, the estimation problem of the FDIAs for the LFC system in the presence of external disturbances is investigated. The LFC system model under FDIAs against frequency and tie-line power measurements is established. Then an attack estimation scheme combining the attack observer (AO) and technique is proposed to minimize the effect of external disturbance on estimation errors. The uniform boundedness of the state and attack estimation errors is proved using Lyapunov stability theory. Finally, a two-area interconnected power system is simulated to demonstrate the effectiveness of the proposed attack estimation algorithms.

Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults

In this paper, the problem of distributed fault-tolerant state estimation is studied for stochastic systems over sensor networks with intermittent sensor faults. Compared with the traditional state estimation algorithms, the distinct advantage of fault-tolerant state estimation is that the estimator can keep good performance whether sensor faults occur or not. Different from the previous literature concerning with distributed fault diagnosis, the distributed fault diagnosis problem is investigated in this paper for intermittent faults, whose appearing time, disappearing time and magnitude are all nondeterministic. The distributed fault-tolerant state estimation scheme is constructed, in which the appearing time and disappearing time of intermittent faults are detected, intermittent faults are estimated and compensated. By means of the matrix inequality technique, the H∞ performance of state estimation errors is guaranteed by properly choosing the estimator parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed algorithm.

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

AbstractThis article considers the issue of finite‐time asynchronous state estimation for event‐triggered nonlinear Markovian jump systems subject to cyber‐attacks. An adaptive event‐triggered scheme is introduced to cope with the capacity constraint of the networked resources. It is assumed that the transmitted sensor measurements may experience randomly malicious cyber‐attacks. Considering the effect of the adaptive event‐triggered scheme and the occurrence of cyber‐attacks, we establish a new state estimation error system model. Sufficient conditions of the finite‐time boundedness and the finite‐time boundedness are developed for the augmented system. Then, the design methods of the asynchronous estimator gains are derived, which can ensure the finite‐time boundedness of the estimation error system. Finally, a numerical example is given to show the effectiveness of the theoretical results.

Finite-time asynchronous state estimation for jump systems with partial transition probabilities via redundant channels: A co-design method

This paper addresses the H∞ finite-time asynchronous state estimation issue for Markov jump systems with partial transition probabilities. A hidden-Markov-chain-based redundant channel model (HMCb-RCM) is established to reflect a more practical situation. Based on the output of the HMCb-RCM, firstly an asynchronous full-order state estimator is devised for the jump system with partial transition probabilities. Then, new sufficient criteria are derived such that the state estimation error is H∞ stochastically finite-time bounded. The relationship between the partial transition probabilities and asynchronous modes is revealed as few attempts. The conditional transition probability matrix (CTPM) for the HMCb-RCM is not fixed but designable accordingly; a co-design strategy is newly developed to synthesize the CTPM and the state estimator simultaneously, which produces less conservatism than that with fixed CTPM. Finally, the theoretical results are applied to a one-link robotic manipulator to validate the proposed results.

Observer‐based consensus control for multi‐agent systems with measurement noises and external disturbances

AbstractThis study presents a distributed observer‐based consensus control for general linear multi‐agent systems under measurement noises and external disturbances. By using the state linear transformation with the matrix constructed from the incidence matrix of a virtual chained directed spanning tree, we transform the observer‐based consensus problem into an asymptotic stability problem of a corresponding augmented linear system. The augmented linear system consists of the reduced‐order system deduced from dynamic equations of the agents and state estimation error system. Based on asymptotic stability of the augmented linear system, we present some sufficient conditions in terms of linear matrix inequalities for the existence of the distributed observer‐based consensus controller. Finally, the effectiveness of the proposed approach is illustrated by a numerical example.

A parameter setting method to find the required state estimation accuracy for a motion control method in merging scenario

AbstractIn this study, we propose a procedure for parameters setting of the motion control method to find the required state estimation accuracy. A lot of motion control methods have been proposed for automatic driving vehicles in recent years to reduce fuel consumption and drivers' mental load. Mode predictive control (MPC) method is one of the often‐used control methods in motion control of automobiles. The technique to find the required state estimation accuracy is necessary to implement a motion control method. However, to the best of the authors' knowledge, such a technique has not been presented in any literature before. Therefore, we proposed a parameter setting method to find the required state estimation accuracy, assuming a model‐predictive‐type motion control method designed based on an optimal control problem. The contributions of this work are as follows: (1) A specific procedure to set the control parameters is proposed. (2) To overcome the difficulty of the parameter setting problem, we divide the control parameters into two sets. The first set of parameters are unrelated to the robustness against state estimation errors, while the others are related. (3) The parameters in the first set are designed independently of the others, so that parameter setting can be easily accomplished. The parameters in the first set are designed to obtain desirable motion of the ego vehicles ignoring the errors. The other parameters are set to find the required state estimation accuracy. (4) The parameter tuning problem and the specification of the state estimation accuracy is considered together. As a result, once the control parameters are determined, the required state estimation accuracy is specified. As an example, a robust motion control method based on MPC for merging scenario was considered. Calculations for some cases were conducted to show the effectiveness of the proposed procedure. The results showed that, if the estimation errors in the displacements of the vehicles are bounded in [− 1, 1][m]and that in the velocities are bounded in [− 1/30, 1/30][m/s], desirable motion control is possible for all the tested initial conditions.

A Linear Recursive State of Power Estimation for Fusion Model Component Analysis with Constant Sampling Time

The state of power of lithium-ion batteries, as the main product of choice for electric and hybrid electric vehicle energy storage systems, is one of the precise feedback control parameters for the battery management system. The proposed research establishes a method for the analysis of charging and discharging constitutive factors under the sampling time, realizes the online identification of parameters by building an adaptive forgetting factor recursive least-squares method based on the Thevenin model, and uses the online parameters to achieve an effective characterization of the power state under voltage and current limitations. The results demonstrate that the accuracy error of online parameter identification is less than 0.03 V. Combining the analysis of charging and discharging constitutive factors under-sampling time with the fusion model of voltage and current limitation makes the power state estimation more reliable and accurate. The results demonstrate that the power state estimation error in the discharging state is less than 8%.

Robust dynamic output feedback control of blood glucose level in diabetic rat with robust descriptor Kalman filter

This paper deals with the robust control problem for a model of blood glucose system in Type-1 diabetic rats with disturbances (food, physical activity, and insulin sensitivity parameter) and model uncertainties. The considered model has a special structure, which can be written as a descriptor model. The robust control law is designed such that the output of the system tracks the desired reference signal despite the parameter uncertainties. By utilizing the μ-synthesis-based and H∞ controller design methods, the desired input signals (insulin for injection) are designed for both approaches and compared with each other. Moreover, the control structure is considered in a way that the states are not essential to be directly measured, and instead, the states estimation is utilized which operates in the closed-loop system together with a control law. Due to the existence of the model uncertainties, external disturbances, and also the characteristics of the descriptor system, the robust descriptor Kalman filter is designed for this system. Finally, to demonstrate the efficiency of the proposed methods, they are applied to the model, and simulations are provided. The results show the closed-loop system including the descriptor model, robust controller, and robust descriptor Kalman filter can force the blood glucose level to maintain in the normal range successfully with an acceptable state estimation error.

Safe learning-based observers for unknown nonlinear systems using Bayesian optimization

In this paper, a modular observer design methodology is formulated for nonlinear systems with partial model knowledge. Our design consists of three phases: (i) an initial robust observer design that enables one to learn the dynamics without allowing the state estimation error to diverge (hence, safe); (ii) a learning phase wherein the unmodeled components are estimated using Gaussian process-based Bayesian optimization; and, (iii) a re-design phase that leverages the learned dynamics to improve convergence rate of the state estimation error. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system. Additionally, certificates of guaranteed estimation performance are provided.

Distributed adaptive formation control for underactuated quadrotors with guaranteed performances

This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances. To ensure a robust and stable formation pattern with predefined behavior bounds, by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, a prescribed performance mechanism is introduced in the translational loop to render the formation regulation as a prior. An adaptive consensus strategy is developed according to undirected graph theory and Lyapunov stability rules for follower quadrotors to achieve a distributed cooperative formation with prescribed tracking abilities via exchanging local information with neighbors. The presented control scheme has the following salient merits: (1) the formation synchronization errors can be guaranteed within pre-assigned bounds with desired transient behaviors despite of uncertain disturbances; (2) by using a state estimation error to update neural network (NN) parameters, rather than the tracking error that widely applied in traditional NN approximators, and with the help of MLP technique, the proposed SE-MLP observer capable of decreasing the computational complexity can achieve a fast identification of lumped disturbances without causing high-frequency oscillations even using a large adaptive gain, and the transient solutions of L2 norm of the differential of neural weights are established to illustrate the mechanism of SE-MLP observer in reducing chattering behaviors. The merits of presented algorithm are confirmed by sufficient simulations.