State Estimation Scheme Research Articles

Magnetic Sensor-Based Multi-Vehicle Data Association

Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.

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.

Stubborn state estimation for nonlinear distributed parameter systems subject to measurement outliers

AbstractIn this article, the state estimation problem is investigated for a class of distributed parameter systems (DPSs). In order to estimate the state of DPSs, we give a partition of spatial interval with a finite sequence and, on each subinterval, one sensor is placed to receive the measurements from the DPS. Due to the unexpected environment changes, the measurements will probably contain some outliers. To eliminate the effects of the possibly occurring outliers, we construct a stubborn state estimator where the innovation is constrained by a saturation function. By using Lyapunov functional, Wirtinger inequality and piecewise integration, some sufficient conditions are obtained under which the resulting estimation error system is exponentially stable and the performance requirement is satisfied. According to the obtained analysis results, the desired state estimator is designed in terms of the solution to a set of matrix inequalities. Finally, a numerical simulation example is given to verify the effectiveness of the proposed state estimation scheme.

Cyber-attacks against cyber-physical power systems security: State estimation, attacks reconstruction and defense strategy

In modern power systems, the connection between cyber part and physical part is more and more close and deeply coupled, while cyber-physical power systems (CPPS) can exactly describe the dynamic process of modern power grids. The problem of secure state estimation and attack reconstruction of cyber-attacks corrupting states of CPPS is addressed. First, the classical small signal model of CPPS under disturbance and cyber-attacks is established. Then, an intermediate observer is proposed to realize the security state estimation and state attack reconstruction of CPPS under cyber-attacks, meanwhile, the linear matrix inequality (LMI) is used to solve the parameters of the intermediate observer. Finally, an attack defense strategy satisfying optimal economic dispatch is proposed. Case studies are presented to assess the effectiveness of intermediate observer reconstruction and the effectiveness of defense strategy.

Estimation for Fuzzy Semi-Markov Jump Systems With Indirectly Accessible Mode Information and Nonideal Data Transmission

This article proposes a novel <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation scheme for a family of Takagi-Sugeno fuzzy semi-Markov jump systems with indirectly accessible mode information and nonideal data transmission. To address estimation of the indirectly accessible modes, the observed-mode sequence emitted by emission probabilities is utilized in this article. By extending the classic Lyapunov stability theory, a set of novel convex stability criteria is proposed by eliminating the nonconvex terms in stabilization conditions with the aid of certain techniques. The proposed stability criteria are utilized to ensure the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the studied fuzzy systems. In addition, numerically checkable conditions on the existence of a fuzzy observed-mode-dependent estimator are formulated to guarantee the σ-error mean square stability of the underlying error system with a guaranteed <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> disturbance attenuation level. The developed theoretical results are illustrated by an application of a single-link robotic arm.

Distributed State Estimation Based Distributed Model Predictive Control

In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

Distributed state estimation for renewable energy microgrids with sensor saturations

In this paper, the distributed state estimation problem is studied for renewable energy microgrids with sensor saturations. A system model for the microgrids with sensor saturations is proposed. Attention is focused on the design of a distributed recursive estimation scheme such that, in the presence of the sensor saturations, an upper bound of the estimation error covariance is guaranteed. Subsequently, such an upper bound is minimized by appropriately designing the gain matrices of the corresponding state estimator. In particular, the sparsity of the gain matrices resulting from network topology is handled by using a matrix simplification method. Moreover, the performance evaluation of the designed distributed state estimator is conducted by analyzing the exponential boundedness of the estimation error in the mean square sense. Finally, simulation experiments under two cases are carried out on a renewable energy microgrid which contains two distributed generation units. The simulation results demonstrate that the developed state estimation scheme is effective.

Penetration-force estimation approach for a flexure-jointed micro-injection mechanism with Lorentz force actuation

This paper presents a novel linear-motion compliant mechanism with Lorentz force actuation and integrated force-sensing capability for automated cell micro-injection. A model-based force estimation approach is introduced such that no force sensor is required. Model identification is undertaken by applying a sinusoidal actuation signal while the mechanism contacts with objects of known stiffness. Displacement data is then used to calculate model coefficients via a least-squares optimization. By using an observer-based state estimation scheme with actuation and displacement signals as inputs, force sensing accuracy within 70 μN RMS error could be achieved within a sensing range of 0 - 5 mN. This sensing capability confirms the suitability of the system for penetration force measurement in certain cases of cell micro-injection.

Over-the-Air Aggregation With Multiple Shared Channels and Graph-Based State Estimation for Industrial IoT Systems

We consider remote state estimation for an industrial Internet-of-Things (IoT) system, where the plant dynamics are monitored by a number of distributed industrial IoT sensors. We propose an “estimation friendly” remote state estimation framework, which not only maintains low computational complexity but also provides better estimation stability performance. Specifically, we propose a novel over-the-air-aggregation-based multiple access, which enhances the observability performance of the state estimation system and hence, provides better estimation stability. Additionally, exploiting the sparsity in the observation matrix induced by the over-the-air-aggregation-based multiple access, we propose a low-complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the quasi-diagonal transformation of the aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact maximum a posterior estimation. Using the Lyapunov drift analysis, we derive the closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. The numerical results demonstrate that the proposed scheme has a low computational complexity. Furthermore, it is scalable with the number of sensors and has a low power consumption.

Spin-state estimation using the Stern-Gerlach experiment

We propose a state estimation scheme for spins, using a modified setup of the Stern-Gerlach experiment, in which a beam of neutral spin-1/2 point particles interacts with a quadrupolar magnetic field. The proposed linear inversion estimation procedure, based on a quadrant intensity detector, requires a suitable initial spatial state of the beam. The statistical characterization of the estimator of the initial spin state allows us not only to associate an error to the estimated parameters, but also to define a measure for comparing estimation procedures corresponding to different Stern-Gerlach setups.

State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results.

Impulsive observer-based design for state estimation of a class of nonlinear singularly perturbed systems with discrete measurements

The state estimation problem of a class of nonlinear singularly perturbed systems with discrete measurements is investigated. The fast dynamics is assumed to be linear and exponentially stable. Under this assumption and some additional conditions, an asymptotic representation of the fast state is obtained in terms of the slow state and the known input. Then the full state estimation problem reduces to the slow state estimation problem. Based on this observation, an impulsive observer based estimation scheme is presented in the framework of singular perturbation. The continuous part of the proposed impulsive observer is a copy of the reduced-order slow dynamics of the observed system, while the impulse part is responsible for the implementation of the sample-triggered update of the observer state whenever a new sample of the output arrives. The estimation error is analyzed via a discontinuous Lyapunov function, and an ε-independent observer gain is designed. It is shown that the resulting estimation error exponentially converges towards an ultimate bound of order O(ε). The theoretical results are successfully applied to state estimates of Chua’s circuit and a flexible link robot. The simulation results demonstrate that the proposed state estimation scheme can achieve a desirable estimation accuracy for small enough ε>0.

DC-link current based position estimation and speed sensorless control of a BLDC motor used for electric vehicle applications

Abstract Sensors of any kind contribute to extra space and electronics when they are used in any application. Besides, the sensor noise also has the effect of altering the overall gain of the system. This is more prevalent in non-linear systems like motor control. In applications which have strict space constraints like Electric vehicles, the use of sensors must be optimized, which, in turn, gave rise to many sensorless state estimation strategies. This paper proposes a novel sensorless control technique for brushless direct current (BLDC) motor used in electric vehicle applications. The concept of sensorless control in BLDC Motor drive eliminates the hall-effect position sensor, thereby giving better performance and improves the robustness of the overall drive system. The main objective of this work is to estimate the position of the motor at standstill condition using stator saturation effect concerning the rotor, accelerate the motor from standstill so that enough back EMF is generated. This acceleration technique speeds up the motor to a stage where a self-actuating control mechanism is used to generate control signals with back EMF or line voltages. The motor can be started with a load, which is a significant constraint for electric vehicle application. The proposed method will avoid the reverse rotation of the motor. The proposed work is simulated in Matlab/Simulink software, and results obtained show that it works well under dynamic conditions of starting, acceleration and load switching. The hardware setup of the proposed work is developed using the TMS320F2812 DSP processor. Simulation and experimental results validate the effectiveness of the proposed work for electric vehicle application.

Novel Techniques for a Verified Simulation of Fractional-Order Differential Equations

Verified simulation techniques have been investigated intensively by researchers who are dealing with ordinary and partial differential equations. Tasks that have been considered in this context are the solution to initial value problems and boundary value problems, parameter identification, as well as the solution of optimal control problems in cases in which bounded uncertainty in parameters and initial conditions are present. In contrast to system models with integer-order derivatives, fractional-order models have not yet gained the same attention if verified solution techniques are desired. In general, verified simulation techniques rely on interval methods, zonotopes, or Taylor model arithmetic and allow for computing guaranteed outer enclosures of the sets of solutions. As such, not only the influence of uncertain but bounded parameters can be accounted for in a guaranteed way. In addition, also round-off and (temporal) truncation errors that inevitably occur in numerical software implementations can be considered in a rigorous manner. This paper presents novel iterative and series-based solution approaches for the case of initial value problems to fractional-order system models, which will form the basic building block for implementing state estimation schemes in continuous-discrete settings, where the system dynamics is assumed as being continuous but measurements are only available at specific discrete sampling instants.

A state estimation strategy for a nonlinear switched system with unknown switching signals

A strategy is presented to estimate the state of a nonlinear autonomous switched system, with no knowledge of the switching signal, except its dwell time. To do so, algorithms to estimate the switching times and the current mode of the system are developed. The estimation of the switching times is based on approximating the second ( generalised) derivative of the output of the system via a convolution of this signal with a suitable function and on detecting the corresponding spikes. To estimate the modes, a scheme based on the use of a bank of observers (one for each mode) and of a bank of subsystems (for each step of the estimation process a suitable subset of the subsystems of the switched system) is developed. The algorithms run regardless of the state observer model, as long as its output error norm decays exponentially with a controlled decay rate.

Linear Distribution System State Estimation with Integration of DG

Monitoring of a power system is essential for maintaining it in a secure operating condition. Electric distribution management system (DMS) should possess a monitoring mechanism addressing the entire operating conditions of the system. Distribution System State Estimation (DSSE) is an integral building block of the DMS. Because of new technologies integrated into the power distribution system, new opportunities, problems and challenges related to control and management are being explored. To make efficient and reliable control actions, an accurate knowledge of the system states is required. This paper presents Linear State Estimator based DSSE which can handle the challenges mentioned above. The estimator studied and reported in the paper allows to include synchronized phasor measurements provided by Phasor Measurement Units (PMUs). With the increasing integration of DG in distribution system, the state estimation scheme needs be reworked to address the complexities introduced by power exchange of DG. To address this, Linear State Estimation (LSE) is reformulated and its performance is analysed with DG integrated in the sample network under study.

Event-triggered H∞ state estimation for time-varying neural networks with variance-constraint and fading measurements

This paper addresses the event-triggered H∞ state estimation problem for a class of discrete recurrent neural networks subject to variance-constraint and fading measurements. The phenomena of fading measurements are described by introducing a set of mutually independent random variables, which reflect that each sensor has individual missing probability. In addition, for the purpose of energy saving, an event-triggered H∞ state estimation scheme is used for time-varying neural networks to determine whether the measurement output is transmitted to the estimator or not. Some sufficient conditions are obtained to guarantee that the estimation error system satisfies both estimation error variance constraint and prescribed H∞ performance requirement. Finally, the feasibility of the proposed event-triggered H∞ state estimation method is verified by a numerical example.

Finite-Time State Estimation for Delayed Neural Networks With Redundant Delayed Channels

The finite-time state estimation issue is addressed in this paper for discrete time-delayed neural networks (NNs). More than one communication channel is utilized to improve the communication performance. The transmission delays of each channel are modeled by a family of stochastic variables which are independent and identically distributed. The main purpose of this paper is to construct an appropriate state estimation scheme under which the corresponding state estimation error dynamics is finite-time bounded in the mean square. By employing the stochastic analysis approach and introducing a special Lyapunov-like functional, we have developed certain sufficient conditions to achieve the prescribed estimation performance. Furthermore, the exact expressions of the achieved estimator parameters are given by solving a special minimization problem subject to certain inequality constraints. Finally, we propose an illustrative simulation to examine the correctness, as well as the effectiveness, of our proposed state estimation method.

Experimental application of individual column state and parameter estimation in SMB processes to an amino acid separation

Abstract The simulated moving bed (SMB) process is a highly efficient continuous chromatographic separation process. Due to its hybrid process dynamics that lead to discontinuities and sharp fronts on the state trajectories, optimal SMB process operation is challenging. Process performance can be improved by applying model-based optimizing control methods. For this, online information about states and individual column parameters are required. The strategy for simultaneous state and parameter estimation used here exploits the switching nature of the SMB process. The successful experimental application of the strategy is demonstrated for the continuous separation of two amino acids on an SMB pilot plant where extra-column equipment effects need to be considered.

State estimation of double-pyramid tethered satellite formations using only two GPS sensors

Recently, space tethered satellite system has attracted widespread attention due to its attractive potential in space applications. Significant research on the tethered satellite formations has been undertaken to study the dynamics and control, vibration modes, tether models, and tether applications and it is generally assumed that the state information of the tethered satellite system is completely known or can be easily measured. However, few studies have been presented on the state estimates of the tethered formations. In this study, a state estimation scheme, with a minimal sensor fusion design, is presented for double-pyramid tethered satellite formations. The objective is achieved by fusing only the measurements of the two GPS sensors and the tether constraints. Furthermore, the observability of the proposed fusion design is proved through the analyses of Lie derivatives. Simulation results are presented that validate the proposed scheme. The proposed scheme provides a general hint of state estimation for a class of satellite formations with the tethered constraints.