Proposed State Estimation Research Articles

Distributed State Estimation of a Non-linear process system with interconnected subsystems

In this paper authors propose a distributed state estimation scheme for the hybrid system with interconnected subsystems to estimate the states. The system considered in this work has different subsystems which can interact with each other via their states over a communication network. The objective is to implement the distributed state estimation scheme for interconnected subsystems in which each subsystem sensors are connected to the communication network, the estimator has been used for each subsystem to estimate the current states by utilizing the states of the neighboring subsystems over the communication network. The proposed state estimation framework utilizes unscented Kalman filter algorithm. Unscented Kalman filter has been designed for each subsystem to estimate the current state estimates which corrupted by state and measurement noise. The benchmark system taken to implement distributed state estimation is continuous stirred tank reactor units which are strongly interconnected via their neighboring states. The reactors temperature is maintained at unstable operating point by a decentralized proportional integral controller for each subsystem by utilizing the measurements of the sensors from the network. The estimation framework has been verified in the absence of the network failure to stabilize the plant at unstable operating point. The estimate of the corresponding subsystems is used to compute the controller output in the absence of the network failure with minimal sharing over the network.

Generalized dissipativity state estimation for genetic regulatory networks with interval time-delay signals and leakage delays

This paper examines the state estimation issue of genetic regulatory networks (GRNs) including time delays and leakage delay term subject to unified dissipativity performance based on the Lyapunov functional approach. The main aim of the proposed state estimator is to access the true concentrations of the proteins and mRNAs, which is explored based on the available output measurements. As a first attempt, the generalized dissipativity concept is implemented for GRNs to generalizing the state estimation technique in the framework of linear matrix inequalities (LMIs) via an improved integral inequality together with the reciprocally convex inequality (RCI) technique. Then, the developed criterion ensures that the estimated error dynamics to be asymptotically stable and extended dissipative in addition to well monitored by the original dynamics via unified dissipativity performance. Finally, an interesting simulation example as a synthetic oscillatory network of transcriptional regulators in Escherichia coli is illustrated to explore the effectiveness and applicability of the developed state estimation technique, along with a comparison study also been exploited by using widely studied example.

State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks

The development of state estimators for local electrical energy supply systems is inevitable as the role of the system’s become more important, especially with the recent increased interest in direct current (DC) microgrids. Proper control and monitoring requires a state estimator that can adapt to the new technologies for DC microgrids. This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network, which until recently has been applied only in forecasting studies. The modified LSTM network for the proposed state estimator adopted a specifically weighted least square (WLS)-based loss function for training. To demonstrate the performance of the proposed state estimator, a comparison study was done with other SE methods included in this paper. The results showed that the proposed state estimator had high accuracy in estimating the states of DC microgrids. Other than the enhanced accuracy, the deep-learning-based state estimator also provided faster computation speeds than the conventional state estimator.

Q‐learning for noise covariance adaptation in extended KALMAN filter

AbstractThe extended Kalman filter (EKF) is a widely used method in navigation applications. The EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice. This paper attempts to suppress the unfavorable effect of the noise covariance uncertainty to the EKF in the framework of reinforcement learning. The proposed state estimation algorithm combines the EKF and a Q‐learning method, where a covariance adaptation strategy is designed based on the Q‐values, leading to a gradual improvement in the estimation performance. The resultant algorithm is called the Q‐learning extended Kalman filter (QLEKF), which is less sensitive to the noise covariance uncertainty. To evaluate the estimation error behavior in nonlinear uncertain systems, the stability of the filtering algorithm is investigated. A numerical simulation for spacecraft autonomous navigation is implemented to demonstrate the efficiency of the QLEKF. It is shown that the presented algorithm outperforms a traditional EKF and an adaptive extended Kalman filter (AEKF) in estimation accuracy.

Situation Projection Based on Multi-time-scale Recursive Dynamic State Estimation

In order to solve the problem of lacking of situation awareness in smart distribution network, this paper proposed a real-time situation projection method for distribution network based on multi-time-scale dynamic state estimation. Based on hybrid measurement including micro PMU, a multi-time-scale recursive dynamic state estimation was realized through recursive transformation between state projection and pseudo measurement. A pseudo measurement Correction algorithm is implemented in the proposed state estimation loop to increase the estimation accuracy. Utilizing the historic states data calculated from the proposed state-measurement recursive dynamic state estimation, a novel real-time situation projection method is proposed in this paper. With the modelling of partitioned multivariate time series analysis, the proposed situation projection method is achieved by predicting the situation indexes for the distribution network operator in real time. Simulation validates the feasibility of the proposed method to improve the situation awareness level in the distribution network.

Data Prediction and Real-time State Estimation of Distribution Network

With the improvement of real-time measurement technology in distribution network, the data collected by power dispatching department is explosively growing. As a result, the real-time detection and analysis of the operation status of distribution network is facing new challenges. In this paper, a prediction model based on the combination of fuzzy c-means (FCM) algorithm and BP neural network is proposed to describe the range of future distribution network operation data. The purpose of this work is to provide a reference range for real-time operation state analysis. Then, considering the problem of massive measurement data in distribution network, a robust state estimation method based on exponential function weighted least squares (EFWLS) is proposed, which has better accuracy and efficiency than traditional weighted least squares (WLS) method. Finally, the accuracy of the prediction model is verified by comparing the predicted data with the real data, and the advantages of the proposed state estimation method are verified by comparing the results of different methods.

Nonlinear State and Parameter Estimation for Li-ion Batteries with Thermal Coupling

Advanced Lithium-ion battery management systems rely on accurate cell-level state of charge (SOC) and parameter estimation for safe and efficient real-time monitoring. However, the design of combined state and parameter estimators that are provably convergent is notoriously difficult. A robust observer framework based on a coupled equivalent circuit-thermal model for a cylindrical battery is proposed. The coupled model also takes into account SOC and temperature-dependent electrical parameters for higher accuracy. In the literature, the model parameters are often treated as constants to simplify model structure and observer analysis. The problem considered in this work is particularly challenging due to (i) nonlinear two-way coupling between electrical and thermal sub-models, and (ii) nonlinear dependence of model parameters on the system states. A single aggregated observer for both SOC and thermal estimation becomes intractable due to lack of convergence certification caused by complex model coupling. We tackle this problem by proposing a sequential estimation scheme such that every sub-estimator converges separately, which is mathematically verified by Lyapunov stability analysis. Simulation results demonstrate the performance of the proposed state and parameters estimation framework.

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.

A state estimator–based nonlinear predictive control for a fractional-order Francis hydraulic turbine governing system

A nonlinear predictive control scheme with a state estimator for a fractional-order hydraulic turbine governing system is studied in this article. First, a more practical fractional-order model of the hydraulic turbine governing system is introduced. Second, according to the Grünwald–Letnikov definition of fractional calculus, the fractional-order hydraulic turbine governing system is transformed into a nonlinear discrete model. Third, based on the Takagi–Sugeno fuzzy theory, a fuzzy prediction model of the fractional-order hydraulic turbine governing system is presented. Furthermore, a state estimator–based nonlinear predictive control scheme is proposed for the fractional-order hydraulic turbine governing system, and the controller gain can be obtained by solving a linear matrix inequality. Finally, experimental results show that the proposed state estimator–based nonlinear predictive control could effectively restrain the oscillation of the fractional-order hydraulic turbine governing system under various working conditions.

Robust pseudo-measurement modeling for three-phase distribution systems state estimation

Trending integration of distributed energy resources calls for state estimation at the distribution level for providing reliable power systems information. In contrast to transmission systems, distribution systems are sparsely monitored, and consequently difficult to estimate states. To address measurement scarcity problem in distribution systems, this paper proposes a distribution system state estimation framework that relies on robust pseudo-measurement modeling. User-level metering data is used to train gradient boosting tree models for generating pseudo-measurements. A ladder iterative state estimator is then applied on the pseudo-measurements to solve for system states. Simulation studies are performed on the IEEE 13-bus and 123-bus test feeders. Numerical results demonstrate that the proposed state estimation scheme outperform two benchmark approaches in terms of accuracy (error), consistency (error variance) and robustness (high accuracy subject to load changes).

Recursive state estimation for two-dimensional shift-varying systems with random parameter perturbation and dynamical bias

In this paper, the state estimation problem is investigated for a class of two-dimensional (2-D) stochastic systems with shift-varying parameters. The stochasticity with the underlying system comes from three sources, namely, random parameter perturbations, dynamical biases and additive white noises. The measurement output is subject to the uniform quantization as a result of communication constraints on the sensors. The purpose of the addressed problem is to recursively estimate the system states with prescribed estimation performance in spite of the stochastic disturbances and the uniform quantizations. An augmented model is first constructed to combine the information about the state and the bias, and the dynamical evolution is subsequently discussed. Furthermore, through stochastic analysis and mathematical induction, the estimator design algorithm is developed to acquire certain upper bound on the estimation error variance and such an upper bound is then minimized in the matrix-trace sense. Moreover, the boundedness issue of the estimation errors is investigated via matrix inversion lemma and inductive analysis technique. Finally, the effectiveness of the proposed state estimation algorithm is validated on a typical 2-D system modeled by the well-known Darboux equation with extensive application potentials in industrial processes.

Finite-time resilient [formula omitted] state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism

In this paper, the finite-time resilient H∞ state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator for the discrete-time delayed neural networks. In order to handle the possible fluctuation of the estimator gain parameters when the state estimator is implemented, a resilient state estimator is adopted. By constructing a Lyapunov–Krasovskii functional, a sufficient condition is established, which guarantees that the estimation error system is bounded and the H∞ performance requirement is satisfied within the finite time. Then, the desired estimator gains are obtained via solving a set of linear matrix inequalities. Finally, a numerical example is employed to illustrate the usefulness of the proposed state estimation scheme.

Dynamic event-triggered mechanism for H∞ non-fragile state estimation of complex networks under randomly occurring sensor saturations

In this paper, the problem of non-fragile H∞ state estimation is investigated for a class of discrete-time complex networks subject to randomly occurring sensor saturations (ROSSs) under a dynamic event-triggered mechanism (DETM). The ROSS phenomenon is taken into account in the network measurements as a reflection of the probabilistic limitation of the physical sensors, and the DETM is implemented to govern the signal transmission from the sensor to its corresponding state estimator. The objective of the problem addressed is to design an H∞ non-fragile state estimator under the DETM that can tolerate the possible gain perturbations, thereby possessing the desired non-fragility. By constructing a novel Lyapunov function, a sufficient condition is established such that the estimation error dynamics is exponentially mean-square stable with a prescribed H∞ performance level, and then the estimator gains are parameterized according to certain matrix inequalities. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation scheme.

Wingman-Based Estimation and Guidance for a Sensorless PN-Guided Pursuer

A novel wingman-based estimation and guidance concept is proposed for a sensorless pursuer. The pursuer is guided towards a maneuvering aerial target using proportional navigation (PN) guidance law. The wingman is assumed to acquire bearings-only measurements of the target and to accurately track the wingman-pursuer relative position. The pursuer-target relative states, needed for the pursuer guidance law implementation, are estimated from the available data to the wingman. The proposed state estimator is implemented using extended Kalman filter equations and transformed wingman's measurements into the moving pursuer frame. Analytical observability analysis of the proposed wingman-based measuring concept suggests an optimal wingman trajectory in terms of the wingman-pursuer relative geometry. The resulting wingman trajectory ensures maximum observability of the pursuer-target line-of-sight (LOS) angle, which is a crucial parameter needed for the PN guidance law implementation. The resulting trajectory can be directly related to the well-known LOS guidance concept. Monte Carlo simulation results validate the analytical findings and demonstrate the potential of the proposed concept.

Constrained ensemble Kalman filter based on Kullback–Leibler divergence

Conventional recursive state estimation procedures cannot handle inequality constraints; therefore, they might result in physically meaningless state estimates. In this work, we develop a novel state estimation technique to incorporate inequality constraints in ensemble Kalman filters (EnKF). For this purpose, we first solve for the unconstrained EnKF state estimation, and then construct a constrained distribution based on the unconstrained one by formulating an optimization problem using the Kullback–Leibler (KL) divergence. The proposed constraint implementation technique is cast as a convex optimization problem involving second order cone constraints which can be solved to global optimality. This constraint implementation step can be integrated into any Gaussian filter. Demonstrative case studies of chemical reaction systems are presented to compare and illustrate the efficacy of the proposed state estimation methodology.

State estimation for neural networks with Markov-based nonuniform sampling: The partly unknown transition probability case

In this paper, the state estimation problem is investigated for a class of discrete-time delayed neural networks. The measurements, before they are received by the state estimator, are sampled and the sampling process is modeled by a Markov chain. In order to cater for more practical engineering, the transition probabilities of the Markov chain are considered to be partially available. A mode-dependent full-order state estimator is constructed and a sufficient condition is obtained under which the estimation error dynamics is exponentially ultimately bounded in the mean square. Meanwhile, an ultimate bound of the estimation error is estimated by seeking a root of an elementary equation. Subsequently, the desired estimators are designed in terms of the solution to a set of linear matrix inequalities. Finally, a numerical simulation example is presented and the desired estimator parameters are solved by using the Matlab toolboxes. The simulation illustrates the effectiveness of the proposed state estimation scheme.

Resilient and Fast State Estimation for Energy Internet: A Data-Based Approach

Like the Internet in the computer industry that shifted from a centralized mainframe to a client-based structure, “energy Internet” was proposed to involve the customers and incorporate renewable energy. To empower such bidirectional communication and power management, the power grid deploys massive smart meters and wireless communication techniques. In this context, false data attacks are more likely to occur in an energy Internet system, and considerable imprecision could be caused for the state estimator. In this paper, resilient and real-time state estimation is proposed. First, we investigate the mechanism of the stealthy false data attack and the countermeasures. Then, based on the accurate and reliable pseudovoltage measurements, a changed measurements-based sequential state estimation is introduced. Finally, both simulation results and practical implementation demonstrate the efficacy and efficiency of the proposed state estimation method for the energy Internet.

A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration

In order to systematically shift existing control and management paradigms in distribution systems to new interoperable communication supported schemes in smart grids, we need to map newly developed use cases to standard reference models like Smart Grid Architecture Model (SGAM). From the other side, any new use cases should be tested and validated ex-ante before being deployed in the real-world system. Considering various types of actors in smart grids, use cases are usually tested using co-simulation platforms. Currently, there is no efficient co-simulation platform which supports interoperability analysis based on SGAM. In this paper, we present our developed test platform which offers a support to design new use cases based on SGAM. We used this platform to develop a new scheme for wide area monitoring of existing distribution systems under growing penetration of Photovoltaic production. Off-the-shelf solutions of state estimation for wide area monitoring are either used for passive distribution grids or applied to the active networks with wide measurement of distributed generators. Our proposed distribution state estimation algorithm does not require wide area measurements and relies on the data provided by a PV simulator we developed. This practical scheme is tested experimentally on a realistic urban distribution grid. The monitoring results shows a very low error rate of about 1 % by using our PV simulator under high penetration of PV with about 30 % error of load forecast. Using our SGAM-based platform, we could propose and examine an Internet-of-Things-based infrastructure to deploy the use case.

Track deformable objects from point clouds with structure preserved registration

Manipulating deformable objects is a challenging task for robots. The major difficulty lies in how to track these objects accurately, robustly, and efficiently, considering they have infinite-dimensional configuration space. To deal with these problems, this paper proposes a novel state estimator to track deformable objects from point clouds. A non-rigid registration method, named structure preserved registration (SPR), is developed to update the estimation by registering the object model towards the current point cloud measurement. Both the local structure and the global topology of the deformable object are considered during registration, which improves the estimation robustness under noise, outliers, and occlusions. The tracking result is further refined by running a dynamic simulation in parallel, which enforces the estimates to satisfy the physical constraints of the object. A series of real-time tracking experiments on 1D objects (ropes) and 2D objects (clothes) are performed to evaluate the proposed state estimator. A wire harness manipulation platform is also introduced where robots can manipulate soft wires to desired shapes and autonomously evaluate the manipulation quality through visual feedback.

A Recursive Approach to Quantized H∞ State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols.

This paper deals with the finite-horizon quantized H∞ state estimation problem for a class of discrete time-varying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-driven flavor of today's biological research, the network measurements (typically gigabytes in size by high-throughput sequencing technologies) are transmitted to a remote state estimator via two independent communication networks of limited bandwidths. To lighten the communication loads and avoid undesired data collisions, the measurement outputs are quantized and then transmitted under two SCPs introduced to schedule the large-scale data transmissions. The purpose of this paper is to design a time-varying state estimator such that the error dynamics of the state estimation satisfies a prescribed H∞ performance requirement over a finite horizon in the presence of nonlinearities, quantization effects, and SCPs. By utilizing the completing-the-square technique, sufficient conditions are derived to ensure the H∞ estimation performance and the parameters of the state estimator are designed by solving coupled backward recursive Riccati difference equations. A numerical example is given to illustrate the effectiveness of the design scheme of the proposed state estimator.