Nonlinear Predictive Filter Research Articles

Road slope estimation for heavy-duty vehicles under the influence of multiple source factors in real complex road environments

Accurate road slope information is of paramount importance for optimizing vehicle control and enhancing energy efficiency. In the context of heavy-duty vehicles (HDVs), the precise estimation of road slope presents a formidable challenge. This challenge is rooted in the coupling between vehicle mass and road slope, the robustness of road slope estimation methods across diverse driving styles, and the generalizability of algorithms to real-world road scenarios. To address these challenges, this paper proposes an innovative road slope estimation scheme that comprehensively considers the influences of multiple source factors in a real-world HDV transportation scenario. Firstly, in order to solve the problem of road slope estimation error under complex road types and different driving styles, a complex road slope estimation method based on the nonlinear predictive filter (NPF) combined with the Cubature Kalman filter (CKF) is constructed. To eliminate the problem of vehicle power fluctuation in special traffic scenarios, we propose a special scenario road slope estimation method based on the combination of a low-pass filter (LPF) and the NPF_CKF algorithm. To address the error problem when using low-cost Inertial Measurement Unit (IMU) sensors, we designed a Kalman filter (KF)-based multi-sensor fusion correction method. Finally, we conduct real-world experiments on complex roads at the test site to verify the accuracy of the proposed estimation method and its insensitivity to model parameters. The robustness and applicability of the proposed method are also verified in free-driving experiments conducted in real-world road scenarios.

Payload-oriented control scheme for rotating payload satellite considering inertia uncertainties and measurement errors

For the remote sensing satellite with an unbalanced rotating payload suspended by an Active Magnetic Bearing (AMB), this paper proposes a payload-oriented control scheme where the high-precision payload attitude control is dominating. Firstly, to suppress the disturbances induced by payload inertia uncertainties and state measurement errors, an integrated framework of parameter identification and nonlinear predictive filtering is proposed to estimate payload inertia parameters and system states from multi-timescale, noise- and drift-contaminated measurement data, breaking the mutual constraint between identification and filter. Secondly, based on the estimation results, the control law and bearing electromagnetic force allocation strategy of the payload-oriented scheme are provided, so that the payload tracks the desired motion and the satellite platform follows payload to prevent the air gap of AMB from exceeding the safety threshold. Finally, the simulations are carried out to verify the advantages of the proposed control scheme in enhancing the payload control precision and isolating the platform vibration.

Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System.

For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters

Nonlinear predictive filter based fault diagnosis of oxygen generation system by using electrolytic water in space station

The oxygen generation system using electrolytic water is one of the important systems in space station which supplies the oxygen for the astronauts from the condensate water, i.e., Sabatier reduction reaction water and urine treated water. During the operation of the space station, the normal operation of the system directly affects the safety of astronauts and the operating costs of the space station, so the fault diagnosis of the system has become a research hotspot. This paper systematically describes the fault diagnosis method of the oxygen generation system using electrolytic water, including the establishment of the fault mode and effects analysis, mathematical model of the key components and the estimation algorithm of unmeasured parameters. For the two direct discriminant indicators of the oxygen production and hydrogen production, because they are unmeasurable and their dynamic models are difficult to be established, this paper proposes a cascade algorithm of nonlinear predictive filtering and low-pass filter to solve the problem. Firstly, the oxygen and hydrogen production are estimated by the predictive filter. And then, the high frequency noise introduced by measurement error is eliminated when the oxygen and hydrogen production signals entered in a low-pass filter. Through the simulation analysis of the normal and fault mode, the correctness and effectiveness of the estimation algorithm are illustrated.

Compensated closed-loop Kalman filtering for nonlinear systems

In this paper, we introduce new state estimation algorithm for nonlinear systems with the measurement loss at output channels. The goal of proposed methodology is to compensate for the output state-measurement losses through an advanced scheme that relies on the nonlinear prediction (NLP) and Kalman filter. Using NLP subsystem, based on Exponential Autoregressive (EXPAR) model, a compensated observation signal is first regenerated and then it is delivered to nonlinear discrete filtering or linearized Kalman filter in the form of closed-loop. Two proposed scheme versions (EXPAR-Substitution and EXPAR-Elimination) are presented in this paper. A two-phase permanent magnet synchronous motor (PMSM) case study is exposed to loss of measurements and then the loss is reconstructed using EXPAR-based schemes to demonstrate some capable results of the proposed algorithm. Simulation results of the proposed algorithm are found to be superior compared to other algorithms in the literature.

Control of Blow-Down Wind Tunnel Using Combined Extended Kalman and Nonlinear Predictive Filters

A blow-down wind tunnel is a typical nonlinear time-varying system facing the coupling effects between the pressure and temperature during the short-time test procedure. The control of blow-down wind tunnels has been discussed for a long time and a satisfactory general solution to this problem is still of interests. This paper aims to model the internal relationship of the state variables of the wind tunnel by using thermodynamic theories. With the developed model, a new control method combining extended Kalman filter (EKF) together with auxiliary nonlinear predictive filter (NPF) is proposed to improve the control performance of the blow-down wind tunnel controller, in terms of accuracy and robustness. The transient coupling effects between the pressure and temperature are fully considered in the proposed approach. The results from the simulation and experiments are consistent and demonstrate that the controller based on EKF combined together with NPF can work better than previously proposed EKF-based controller.

Stochastic stability of cubature predictive filter

In this paper, the cubature predictive filter (CPF) is derived based on a third-degree spherical-radial cubature rule. It provides a set of cubature-points scaling linearly with the state-vector dimension, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear predictive filter (PF). In order to facilitate the new method, the algorithm CPF is given firstly. Then, the theoretical analyses demonstrate that the estimated accuracy of the model error and system for the proposed CPF is higher than that of the traditional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of CPF for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system’s initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the CPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system.

Federated nonlinear predictive filtering for the gyroless attitude determination system

This paper presents a federated nonlinear predictive filter (NPF) for the gyroless attitude determination system with star sensor and Global Positioning System (GPS) sensor. This approach combines the good qualities of both the NPF and federated filter. In order to combine them, the equivalence relationship between the NPF and classical Kalman filter (KF) is demonstrated from algorithm structure and estimation criterion. The main features of this approach include a nonlinear predictive filtering algorithm to estimate uncertain model errors and determine the spacecraft attitude by using attitude kinematics and dynamics, and a federated filtering algorithm to process measurement data from multiple attitude sensors. Moreover, a fault detection and isolation algorithm is applied to the proposed federated NPF to improve the estimation accuracy even when one sensor fails. Numerical examples are given to verify the navigation performance and fault-tolerant performance of the proposed federated nonlinear predictive attitude determination algorithm.

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

Nonlinear Multi-Time-Delay Stochastic Estimation: Application to Cavity Flow and Turbulent Channel Flow

A nonlinear extension of the multi-time-delay stochastic estimation technique is presented. The proposed approach consists of the design of nonlinear prediction filters based on artificial neural networks or, for smaller problems, on Volterra expansions of the measured wall variable. The application to two different flows is discussed. The first case is the estimation of the temporal dynamics of the velocity fluctuations in a cavity shear layer in low subsonic conditions from wall-pressure measurements. The second case is the estimation of the streamwise velocity fluctuations in the buffer layer of a fully developed turbulent channel flow from wall shear stress measurements. It is shown that the accuracy of the nonlinear technique is application dependent as it is significantly affected by the underlying nonlinear nature of the flow investigated. In particular, we show that, for the cavity shear layer case, the improvement is marginal and it does not appear to be worth the additional computational complexity associated with the nonlinear problem. On the other hand, the improvement in accuracy of the nonlinear estimation of the velocity fluctuations in the wall turbulence case is significant, owing to the strong nonlinearity of the dynamics in the wall-bounded flow.

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

With respect to cell-to-cell variation in battery packs for electric vehicles (EVs), the estimation of state of charge (SOC) and state of health (SOH) of battery systems remains a challenging problem that needs to be solved under the strict computational limitations of battery management system. This paper aims at proposing a stable and accurate SOC/SOH estimation framework for battery pack with multi-cells connected in series with passive balance control. First, the concepts of cell-level and pack-level state definitions, which clearly describe the relationship between the states of the battery cells and those of the battery pack, are introduced. Then a multi time-scale framework for estimating the SOC/SOH of pack is developed. Within the framework, the SOH values (slow dynamics) are estimated with long time interval, while the SOC (fast dynamics) is estimated in real-time. For the framework implementation, nonlinear predictive filter (NPF) is used as the estimation algorithm to provide accurate estimates of SOC and SOH. Finally, experiments are conducted on a battery pack under UDDS driving cycles to validate the performance of the proposed framework. The experimental validation indicates that the SOC and SOH of the battery pack can be accurately estimated using the proposed framework.

Suppression Effect of α-Cut Based Inference on Consequence Deviations

This paper clarifies that inference based on α-cut and generalized mean (α-GEMII) is effective in suppressing consequence deviations. The suppression effect of α-GEMII is numerically evaluated in comparison to conventional inference based on the Compositional Rule of Inference (CRI). CRI-based parallel inference causes discontinuous deviations in the least upper and greatest lower bounds of deduced fuzzy sets even when it models the continuous input-output relation of a system and given facts change continuously. In contrast, α-GEMII can suppress the deviations because of its schemes originally developed for constraint propagation control. In simulations, indices are defined for numerically evaluating the degree to which deduced consequences follow the change in fuzzy outputs of given systems. Simulation results show that α-GEMII is effective in suppressing the deviations, compared to CRI-based parallel inference. In effective use of the schemes for suppressing the consequence deviations, α-GEMII can be applied to nonlinear prediction filters for complex time series, especially with fluctuations that do not always originate from a correlation between time series data.

Performance Analysis of Nonlinear-Predictive-Filter-Based Chaotic Synchronization

Synchronization of chaotic systems is essential for chaotic communication schemes. If the communication environment is modeled stochastic, the extended-Kalman-filter-(EKF)-based synchronization scheme possesses minimum error in estimating the states for certain chaotic systems/maps. However, its intrinsic nonlinear approximation error can cause divergence and, hence, desynchronization. In this brief, a nonlinear predictive filter (NPF) is proposed for chaotic synchronization. The condition for stability and an approximate expression for the total normalized mean square error are derived. Numerical evaluations reveal that the proposed NPF-based synchronization scheme has better performance compared with the EKF-based scheme

Attitude Determination Methods Using Pseudolite Signal Phase Measurements

ABSTRACT: In this paper, new attitude determination algorithms using pseudolite signal phase measurements are developed and presented with realistic simulations. Pseudolite signals are used to replace GPS signals which are often not available due to blocking by nearby large structures, for example, a crew return vehicle under the International Space Station. A new observation model needs to be applied because spherical wavefronts are present for pseudolite signals, which are much closer to the antennas than GPS signals. Both static and filtering methods are developed. An iterative algorithm using the Levenberg-Marquardt method and a nonlinear Predictive filter provide a deterministic solution; however, they are not suitable for moving cases. Instead, the nonlinear Predictive filter, the extended Kalman filter and the Unscented filter using angular velocity measurements as well as pseudolite signal phase measurements are developed. In the convergence comparison, the Unscented filter and the Predictive filter show more robust behavior than that of the extended Kalman filter.

Chaotic synchronization: A nonlinear predictive filtering approach

The synchronization of chaotic systems is a difficult task due to their sensitive dependence on the initial conditions. Perfect synchronization is almost impossible when noise is present in the system. One of the well known stochastic filtering algorithms that is used to synchronize chaotic systems in the presence of noise is the extended Kalman filter (EKF). However, for highly nonlinear systems, the approximation error introduced by the EKF has been shown to be relatively high. In this paper, a nonlinear predictive filter (NPF) is proposed for synchronizing chaotic systems. In this scheme, it is not required to approximate the underlying nonlinearity and hence there is no need to compute the Jacobian of the chaotic system. Numerical simulations are carried out to compare the performances of the NPF and EKF algorithms for synchronizing different sets of chaotic systems and/or maps. The well known Lorenz and Mackey-Glass systems as well as Ikeda map are used for numerical evaluation of the performance. Results clearly show that the NPF based approach is superior to the EKF based approach in terms of the normalized mean square error (NMSE), total NMSE, and the time taken for synchronization (measured in terms of the normalized instantaneous square error) for all the systems and/or maps considered.

Asymptotic Local Approach in Fault Detection Based on Predictive Filters

Faults diagnosis techniques are important to improve the reliability of practical systems. A fault diagnosis technique is proposed based on a nonlinear predictive filter (PF) that estimates the modeling errors and uses them to correct the estimate of the system states. Under normal operation conditions, the modeling errors contain mainly the modeling uncertainties. However, if a component, an actuator, or a sensor fault occurs, the modeling errors will depart from the normal pattern. Therefore, fault detection is reduced to detecting irregularities in the modeling errors generated by the PF. The asymptotic local approach is used here to detect faults from the modeling errors because of its ability to detect small or incipient faults. The threshold for the fault diagnosis test can be obtained from the x 2 table for a selected confidence level. To demonstrate the performance of the proposed fault diagnosis technique, it is applied to detect abrupt and incipient faults in the satellite attitude measurement system.

A nonlinear adaptive filter for narrowband interference mitigation in spread spectrum systems

This paper presents a novel approach based on the estimation of the jammer instantaneous frequency for the excision of narrowband interferences in spread spectrum systems. Using a three-coefficient finite impulse response (FIR) model for the interference, an augmented state-space representation of the received signal (spread spectrum signal+noise+interference signal) is developed. Based on the state-space representation developed, a Kalman-type nonlinear adaptive filter, namely, augmented-state approximate-conditional-mean (ASACM) filter, is formulated to estimate the unknown jammer instantaneous frequency from the received signal. The receiver performance and the problem of stability of the proposed filter are addressed. The improvement in the performance achieved with the proposed nonlinear Kalman-type filter is quantified in comparison with the linear one based on three-coefficient FIR excision filter, and an adaptive nonlinear prediction filter. The efficacy of the proposed filter is corroborated with simulation examples for interference suppression in spread spectrum systems. Simulation results show that the proposed filter is effective in suppressing powerful narrowband interference in spread spectrum systems with good receiver output SNRs.

NONLINEAR ADAPTIVE PREDICTION OF CHAOTIC TIME SERIES WITH A REDUCED PARAMETER NO NLINEAR ADAPTIVE FILTER

Based on the deterministic and nonlinear characterization of the chaotic signals, a new reduced parameter nonlinear adaptive filter is proposed to make adaptive predictions of chaotic time series. The sigmoid function is introduced to nonlinear predictive filter for reducing unknown parameters of the second-order Volterra filters. A reduced parameter nonlinear adaptive filtering prediction schemeis suggested in order to track current chaotic trajectory by using precedent predictive error for adjusting filter parameters rather than approximating global o r local map of chaotic series. Experimental results show that this reduced param eter nonlinear adaptive filter, which is only trained with 50 samples and 20 ite rations, can be successfully used to make one-step and multi-step predictions of chaotic time series.

Efficient and Optimal Attitude Determination Using Recursive Global Positioning System Signal Operations

In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.

Approximate analysis of narrow-band interference rejection technique in CDMA spread spectrum systems using nonlinear prediction filters

The performance of code-division multiple access (CDMA) spread spectrum systems using nonlinear prediction filters in the presence of narrow-band interference is approximately analyzed. Closed-form approximate analytical expressions for the performance improvement are derived. Also, the closed-form approximate analytical results are compared with simulated results by numerical examples. The comparison results show that the analytical results are valid when the interference bandwidth is small.