The extent of vibrations experienced by a vehicle driving over natural terrain defines its ride quality. Generally, surface irregularities, ranging from single discontinuities to random variations of the elevation profile, act as a major source of excitation that induces vibrations in the vehicle body through the tire-soil interaction and suspension system. Therefore, the ride response of off-road vehicles is tightly connected with the ground properties. The objective of this research is to develop a model-based observer that estimates automatically terrain parameters using available onboard sensors. Two acceleration signals, one coming from the vehicle body and one from the wheel suspension, are fed into a dynamic vehicle model that takes into account tire/terrain interaction to estimate ground properties. To solve the resulting nonlinear simultaneous state and parameter estimation problem, the cubature Kalman filter is used, which is shown to outperform the standard extended Kalman filter in terms of accuracy and stability. An extensive set of simulation tests is presented to assess the performance of the proposed estimator under various surface roughness and deformability conditions. Results show the potential of the proposed observer to estimate automatically terrain properties during operations that could be implemented onboard of a general family of intelligent vehicles, ranging from off-road high-speed passenger cars to lightweight and low-speed planetary rovers.
This letter explores covariance matching-based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of conventional CKF. In the proposed algorithm, weights are adaptively adjusted and used for updating the measurement noise covariance matrices online. It can also enhance the adaptive capability of the ACKF. The simulation results are illustrated to evaluate the performance of the proposed algorithm.
This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, ${\bf \hat P_{k}}^{-}$ based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, ${\bf \hat R_{k}^{*}}$ is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, ${\bf \hat Q_{k}^{*}}$ and measurement noise covariance matrices by …
Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；Department of Electrical and Computer Engineering, University of Central Florida（中佛罗里达大学电气与计算机工程系）；State Grid Heibei Economic Research Institute（国网河北经济研究院）
Cubature Kalman Filter (CKF) has good performance when handling nonlinear dynamic state estimations. However, it cannot work well in non-Gaussian noise and bad data environment due to the lack of auto-adaptive ability to measure noise statistics on line. In order to address the problem of behavioral decline and divergence when measure noise statistics deviate prior noise statistics, a new robust CKF (RCKF) algorithm is developed by combining the Huber's M-estimation theory with the classical CKF, and thereby it is proposed to coping with the dynamic state estimation of synchronous generators i…
Mathematical modeling and real-time dynamics identification of the mean arterial blood pressure (MAP) response of a patient to vasoactive drug infusion can provide a reliable tool for automated drug administration and therefore, reduce the emergency costs and significantly benefit the patient's MAP regulation in an intensive care unit. To this end, a dynamic first-order linear parameter-varying (LPV) model with varying parameters and varying input delay is considered to capture the MAP response dynamics. Such a model effectively addresses the complexity and the intra- and inter-patient variabi…
To achieve robust and accurate state estimation for robot navigation, we propose a novel Visual Inertial Odometry(VIO) algorithm with line features upon the theory of invariant Kalman filtering and Cubature Kalman Filter (CKF). In contrast with conventional CKF, the state of the filter is constructed by a high dimensional Matrix Lie group and the uncertainty is represented using Lie algebra. To improve the robustness of system in challenging scenes, e.g. low-texture or illumination changing environments, line features are brought into the state variable. In the proposed algorithm, exponential …
Northeastern University（东北大学）；The United States Military Academy（美国军事学院）；University of Missouri（密苏里大学）；Florida International University（佛罗里达国际大学）
Modeling biological dynamical systems is challenging due to the interdependence of different system components, some of which are not fully understood. To fill existing gaps in our ability to mechanistically model physiological systems, we propose to combine neural networks with physics-based models. Specifically, we demonstrate how we can approximate missing ordinary differential equations (ODEs) coupled with known ODEs using Bayesian filtering techniques to train the model parameters and simultaneously estimate dynamic state variables. As a study case we leverage a well-understood model for …
In this paper, a novel hybrid-degree dual estimation approach based on cubature rules and cubature-based nonlinear filters is proposed for fault diagnosis of nonlinear systems through simultaneous state and time-varying parameter estimation. Our proposed dual nonlinear filtering scheme is developed based on case-dependent cubature rules that are motivated by the following observations and facts, namely (i) dynamic characteristics of nonlinear system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the sy…
We propose a new extension of Kalman filtering for continuous-discrete systems with nonlinear state-space models that we name as the level set Kalman filter (LSKF). The LSKF assumes the probability distribution can be approximated as a Gaussian, and updates the Gaussian distribution through a time-update step and a measurement-update step. The LSKF improves the time-update step when compared to existing methods, such as the continuous-discrete cubature Kalman filter (CD-CKF) by reformulating the underlying Fokker-Planck equation as an ordinary differential equation for the Gaussian, thereby av…
Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators. Especially, false data injection (FDI) attacks deteriorate the accuracy of state estimation by injecting the false data into the measurement device. In this regard, this paper proposes for the first time an FDI attack model based on the dynamic state estimation of generators. Firstly, Taylor's formula was used to linearize the generator's measurement equation. Se…
In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order su…
Our goal is to send legged robots into challenging, unstructured terrains that wheeled systems cannot traverse. Moreover, precise estimation of the robot's position and orientation in rough terrain is especially difficult. To address this problem, we introduce a new state estimation algorithm which we term Contact-Centric Leg Odometry (COCLO). This new estimator uses a Square Root Unscented Kalman Filter (SR-UKF) to fuse multiple proprioceptive sensors available on a legged robot. In contrast to IMU-centric filtering approaches, COCLO formulates prediction and measurement models according to t…
