Improvement Of State Estimation Research Articles

Effect of Noise Covariance Matrices on State of Charge Estimation Using Extended Kalman Filter

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

Extended moving horizon estimation for chemical processes under non‐Gaussian noises

AbstractStudies on moving horizon estimation (MHE) for applications featuring process uncertainties and measurement noises that follow time‐dependent non‐Gaussian distributions are absent from the literature. An extended version of MHE (EMHE) is proposed here to improve the estimation for a general class of non‐Gaussian process uncertainties and measurement noises at no significant additional computational costs. Gaussian mixture models are introduced to the proposed EMHE to approximate offline the non‐Gaussian densities of these random variables. Moreover, the proposed EMHE‐based estimation scheme can be updated online by re‐approximating the corresponding Gaussian mixture models when the distributions of noises/uncertainties change due to sudden or seasonal changes in the operating conditions. These updates are not expected to increase the central processing unit times considerably. Illustrative case studies featuring open‐loop operation and closed‐loop control using nonlinear model predictive control have shown that the practical features offered by EMHE resulted in significant improvements in state estimation and online control.

Probabilistic Modeling and Bayesian Filtering for Improved State Estimation for Soft Robots

State estimation is one of the key requirements in robot control, which has been achieved by kinematic and dynamic models combined with motion sensors in traditional robotics. However, it is challenging to acquire accurate proprioceptive information in soft robots due to relatively high noise levels and hysteretic responses of soft actuators and sensors. In this article, we propose a method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network. This approach is useful in constructing a state observer for soft robot control when both the kinematic model of the actuator and the model of the sensor are used. In our method, we regard a hysteresis function as a conditional random process model. We then introduce a dynamic Bayesian network composed of the actuator and the sensor models of the target system using distribution hysteresis mapping. Finally, we show that solving a Bayesian filtering problem is equivalent to suboptimal state estimation of the soft system. This article describes two ways for defining modeling and filtering; one is by Gaussian process regression combined with an extended Kalman filter, and the other is based on variational inference with a particle filter. While the first approach relaxes the uncertainty level in modeling to Gaussian, the second approach illustrates a general probability distribution. We experimentally validate the proposed methods through real-time state estimation of a sensor-integrated soft robotic gripper. The result shows significant improvement in state estimation compared to conventional estimation methods.

Adaptive Kalman Filtering for Dynamic Positioning of Marine Vessels

Dynamic Positioning systems usually include wave filtering functionalities to reduce control actions, and hence wear and tear of the propulsion system. Different approaches for design and parametrization of the wave filter components have been investigated through the years. Anyhow, tuning of these algorithms, especially Kalman filter, is a crucial task for proper function, but systematic guidelines or algorithms are not existing. This paper proposes a method for auto-tuning of Kalman filter for Dynamic Positioning of marine vessels using adaptive algorithms. Existing methods of residual- and innovation-based estimation of covariance matrices are incorporated into a linear Kalman filter and evaluated in simulations. The resulting tuning shows significant improvements in state estimation.

Adaptive ensemble Kalman filtering of non-linear systems

A necessary ingredient of an ensemble Kalman filter (EnKF) is covariance inflation, used to control filter divergence and compensate for model error. There is an on-going search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra (1970, 1972) enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the model error and observation covariances. We propose an adaptive scheme, based on lifting Mehra's idea to the non-linear case, that recovers the model error and observation noise covariances in simple cases, and in more complicated cases, results in a natural additive inflation that improves state estimation. It can be incorporated into non-linear filters such as the extended Kalman filter (EKF), the EnKF and their localised versions. We test the adaptive EnKF on a 40-dimensional Lorenz96 model and show the significant improvements in state estimation that are possible. We also discuss the extent to which such an adaptive filter can compensate for model error, and demonstrate the use of localisation to reduce ensemble sizes for large problems.

Robust vision-aided inertial navigation algorithm via entropy-like relative pose estimation

The paper presents a loosely coupled approach for the improvement of state estimation in autonomous inertial navigation, augmented via image-based relative motion estimation. The proposed approach uses a novel pose estimation technique based on the minimization of an entropy-like cost function which is robust by nature to the presence of noise and outliers in the visual features. Experimental evidence of the performance of this approach is given and compared to a state-of-the-art algorithm. An indirect Kalman filter is used for navigation in the framework of stochastic cloning. The robust relative pose estimation given by our novel technique is used to feed a relative position fix to the navigation filter. The simulation results are presented and compared with the results obtained via the classical RANSAC-based direct linear transform approach.

Scheduling semiconductor manufacturing processes to enhance system identification

Semiconductor manufacturing is characterized by a manufacturing line with multiple processing tools, products, and other sources of variation. Run-to-run control applications need a constant stream of information about the state of the process in order to perform well. The trace of the state error covariance matrix from the Kalman filter is used as a metric for determining the information content of a particular data set to the run-to-run control algorithm. Processing decisions such as batch scheduling, equipment allocation, and sampling plans are shown to have an effect on estimator performance. Algorithms using the state error covariance matrix are developed that can optimize the factory schedule in order to provide run-to-run control algorithms with the best possible information. Simulation results demonstrate that measurable improvements in state estimation and control output performance can be achieved by using information from the process estimator.

Motion and Force Control of Flexible Master-Slave System (Improvement of State Estimation During Contacting with Objects)

We study the motion, vibration and contact force control of the flexible master-slave system (FMSS). In this study, the master arm is a one-link arm that consists of a rigid body and the slave arm is a one-link arm that consists of a flexible link. The bilateral control based on passivity and the optimal control based on the linear quadratic regulator (LQR) are applied to the system. Each of those control methods has some advantages and disadvantages. We employ the state observer to construct those controllers. However, the accuracy of state estimation is deteriorated while the slave-arm contacts with objects. Therefore we improve the accuracy of the state estimation by applying the disturbance-state observer. We indicate the performance of the present method by experimental results.

Improved state estimator in the face of unreliable parameters

Abstract The improvement in state estimation based on independently estimated parameter values can be marginal when these parameter values are in error. A measure of uncertainty in the parameters is their error covariance, which most parameter estimation methods do not yield reliably. This work develops a new approach that obviates reliance on incorrect error covariance of the parameters. Uncertainty in the parameter values is assessed on the basis of errors in a priori predictions over a certain time horizon. This uncertainty measure is incorporated into the state estimator, modifying the state gains to account for errors in the parameter values used.