Parameter estimation of a three-axis spacecraft simulator using recursive least-squares approach with tracking differentiator and Extended Kalman Filter

STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS

As a new type of guidance technology, the strapdown imaging guidance technology, which helps improve the system reliability and reduce costs efficiently, has been paid great attention and developed quickly. However, the detecting information of strapdown seeker can’t be used to proportional navigation directly because it is coupled with missile attitude. A method to estimate the inertial Line-of-Sight (LOS) rate of strapdown imaging seeker based on Cubature Kalman filter (CKF) and Tracking-Differentiator (TD) was presented. As there were high nonlinearity in both state and measurement equations and more serious non-Gaussian noise in the measurements, the Extended Kalman filter (EKF) could not completely meet the requirements of filtering. Compared with EKF and Unscented Particle filter (UKF), CKF was a congruent method for states estimating in the conditions of nonlinearity and non-Gaussian noise. CKF was applied to estimate the LOS rate of strapdown imaging seeker. Because measurement noise of missile attitude will be reflected in estimation result, TD was used to decrease noise of the missile attitude measurements for improving the estimation precision. Monte Carlo simulation results show that the proposed method can improve the precision of guidance.

To address the demand for enhanced dynamic performance of multi-axle distributed-drive special vehicles in complex terrains, this paper proposes a longitudinal torque coordination control architecture integrating dynamic parameter estimation and multi-objective collaborative optimization. By analyzing the longitudinal dynamic model during ramp driving, a joint gradient resistance estimation module based on Recursive Least Squares (RLS) and Extended Kalman Filter (EKF) is designed to provide critical parameter inputs for the control strategy. Leveraging the independent wheel torque control capability of distributed drive systems and considering wheel load distribution during slope traversal, a tire load rate-based driving torque pre-allocation principle is established. Furthermore, wheel slip phenomena under low-adhesion heterogeneous slope conditions during both driving and starting are analyzed, leading to the development of individual wheel torque control strategies based on slip ratio and angular acceleration. These strategies are integrated with a vehicle-level torque distribution strategy based on tire load rates to achieve secondary optimized torque allocation. Simulation results demonstrate that the proposed control strategy significantly improves dynamic performance and stability while reducing instability risks in extreme operating conditions.

Accurate state of charge (SOC) estimation is the basis of the battery management system in electric vehicles. In order to reduce the influence of time-varying model parameters on SOC estimation accuracy for lithium-ion batteries under complex operating conditions, this paper proposes an improved method for online model parameter identification using variable forgetting factor recursive least squares (VFFRLS), which is based on the sliding window time-varying forgetting factor theory. The VFFRLS was used for online parameter identification at the macroscopic timescale, and extended Kalman filter (EKF) was used for estimating the battery SOC at the microscopic timescale. The accuracy of parameter identification was verified using pulsed discharging and urban dynamometer driving schedule (UDDS) tests, and recursive least squares (RLS) was used to identify the parameters of lithium-ion batteries under UDDS test and estimate the SOC of lithium-ion batteries in combination with EKF, and the experimental results verify the accuracy and robustness of VFFRLS-EKF.

We describe the use of a recursive least-squares approach for calibration of the characteristic frequency of an optical tweezer. Unlike commonly used off-line calibration methods, the RLS algorithm does not require data averaging to reduce the effects of Brownian motion. We use computer simulations to demonstrate that the RLS algorithm can be used to efficiently calibrate the characteristic frequency of a trapped, 10-micron diameter polystyrene bead. However, experimental results suggest that applying the RLS method to an actual optical tweezer system is more difficult because of the measurement errors and laboratory noise.

In speech system identification, linear predictive coding (LPC) model is often employed due to its simple yet powerful representation of speech production model. However, the accuracy of LPC model often depends on the number and quality of past speech samples that are fed into the model; and it becomes a problem when past speech samples are not widely available or corrupted by noise. In this paper, fuzzy system is integrated into the LPC model using the recursive least-squares approach, where the fuzzy parameters are used to characterize the given speech samples. This transformed domain LPC model is called the FRLS-LPC model, in which its performance depends on the fuzzy rules and membership functions defined by the user. Based on the simulations, the FRLS-LPC model with this special property is shown to outperform the LPC model. Under the condition of limited past speech samples, simulation result shows that the synthetic speech produced by the FRLS-LPC model is better than those produced by the LPC model in terms of prediction error. Furthermore with corrupted past speech samples, the FRLS-LPC model is able to provide better reconstructed speech while the LPC model is failed to do so.

Adaptive multi-body yawing filter of wave driven robot with dynamic response amending

State-of-charge estimation approach of lithium-ion batteries using an improved extended Kalman filter

An estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is presented to the position and velocity estimation using angle-only measurements extracted from multiple maneuverable aircrafts with onboard passive sensor. The algorithm is a hierarchical architecture which consists of several local processors and a global processor. In each local processor, an extended Kalman (EK) filter utilizes the reference Cartesian coordinate (RCC) system for state and state covariance extrapolation and utilizes the modified spherical coordinate (MSC) system for state and state covariance updating. In the global processor, a recursive least squares (RLS) estimator sequentially computes a global estimate in the local inertial Cartesian coordinate (LICC) system. The estimator is developed by utilizing RCC updated state covariance to compute each filter weight for combining the outputs of local processors. The typical case of target motion analysis is investigated through computer simulation. In simulation study the proposed algorithm is compared to each HC EK filter. The EK filter encounters slow convergence problem under both circular arc and waving flight scenarios. By using a RLS estimator, the convergence is greatly accelerated. The algorithm markedly improves the tracking accuracy as well.

Traditional approaches to characterizing the transformation from a visual stimulus to neural response assume that 1) the stimulus is stationary and uncorrelated, and 2) the functional properties of the encoding do not change over time. However, recent studies of sensory function under natural stimulus conditions have demonstrated important features of neural encoding that are in violation of these assumptions. Characterizing the stimulus/response mapping in a natural setting demands a more realistic model of sensory encoding in which stimuli of arbitrary complexity are adaptively filtered into a neural response. To identify the stimulus/response mapping in this context, a new analytic technique must be developed. In this paper, a point process extended recursive least-squares (ERLS) approach to receptive field (RF) estimation is detailed. Simulated and experimental neural responses are used to demonstrate the ability of the ERLS technique to estimate RFs from responses to complex natural stimuli and track adaptation of receptive field properties during a single trial. The ERLS technique lends tremendous flexibility to experimental design, facilitating the investigation of sensory function in the natural environment.

8 - Adaptive Glucose Prediction Models

Lithium-ion power battery is the power source of many electronic control systems. The battery state of charge (SOC) and state of health (SOH) are very important to the safety and reliability of the electronic system. Therefore, high-precision estimation of battery SOC and SOH has become one of the current research hotspots in the battery field. In order to realize the state estimation of lithium-ion power battery, an equivalent circuit model of lithium-ion power battery is established. This article is based on the HPPC pulse experiment data and the second-order Thevenin equivalent circuit model. At different temperatures (0°C, 25°C, 45°C), the recursive least square (RLS) method is used to identify of parameters of the equivalent model circuit. The state equation and measurement equation of the double extended Kalman filter (DEKF) algorithm are used to estimate SOC and SOH. Finally, this article uses experimental data of the single cell to estimate the battery state. The accuracy and adaptability of the combination of least squares algorithm and dual extended Kalman filter are verified. This is of great significance for improving the safety, reliability and economic benefits of the battery system.

As permanent magnet synchronous motors (PMSM) have high power density, efficiency, good dynamic performance, and small size they are becoming popular in electric vehicle (EV) applications. Control performance and the efficiency of the system get affected due to electrical, mechanical parameters. Parameters value gets affected by voltage source inverter (VSI) non-linearities, temperature and magnetic saturation effects. If exact parameters for particular torque speed requirement are found, the efficiency of system increases. There are various offline and online methods for finding parameters. Offline methods are easy to implement but requires extra setup and estimate parameters in steady state. Because the effects of transient conditions are taken into account during identification, online methods for obtaining real-time data under running conditions are becoming more popular. An overview about online numerical methods to estimate electrical parameters of PMSM is given. It discusses difference between various methods in terms of computational cost, convergence speed, noise and identification error. Choosing of method will be easy using this work. For inductance estimation, the extended Kalman filter (EKF) algorithm has an identification error of 0.24% under temperature effect and -0.3% under VSI non-linearities effect. The identification error for Rs and ψf using the recursive least square (RLS) method is 0.5% and 0.02%, respectively, when temperature is considered. EKF and RLS algorithms are proposed.

A guidance system architecture for intercepting a manoeuvring target using a monocular imaging seeker is discussed in this paper. The relative interceptor-target kinematics based on the image information is established. A tracking differentiator (TD) based maneuvering target acceleration model is presented. The relative kinematic states can be obtained by combining this model with the extended Kalman filter, as the maneuvering mode of the target is unknown. In view of the maneuvring acceleration of the target can be treated as a time-varying and bounded perturbation, an adaptive guidance law is derived that guarantees the LOS rate to be global ultimate bounded in the presence of measurement noise during the terminal course. Simulation results show that the proposed guidance system architecture is effective and robust to the target maneuver.

: The ground-based spacecraft simulator is a useful tool to develop and verify new control laws required by modern spacecraft applications. In order to simulate the space environment with the ground-based spacecraft simulator, the effects of gravity should be minimized. In this paper, a three-axis rotational rigid spacecraft simulator on a spherical air-bearing system is considered. The method of estimating the inertial properties of a spacecraft as well as the location of the center of mass is presented with the batch least- square estimation. The identified center of mass location is used to actuate the automatic mass balancing system to compensate for the center of mass offset. Adaptive control of the automatic mass balancing system is also presented when the balancing masses are actuated in real-time to eliminate the center of mass offset from the center of rotation. The proposed technique is implemented on the Three Axis Simulator 2 (TAS2) which is a ground-based experimental testbed for the Bifocal Relay Mirror Spacecraft (BRMS).