Showing papers on "Recursive least squares filter published in 2020"

Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

Abstract: Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.

A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

Abstract: Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

State-of-Charge Observer Design for Batteries With Online Model Parameter Identification: A Robust Approach

Abstract: The state-of-charge (SOC) indicates a lithium-ion battery's remaining capacity, and an accurate SOC estimation plays a crucial role in the battery's operation optimization and lifetime extension. This article studies a robust model-based SOC estimation strategy for batteries. Based on a battery equivalent circuit model, a robust recursive-least-squares algorithm is utilized for the model parameters online extraction, which avoids unnecessary experiments prior to SOC estimation for parameter identification. Compared with the conventional recursive least squares, it can effectively guarantee the parameter identification performance in spite of outliers in battery measurement signals. Then, a robust observer with the estimated model parameters is designed for the battery's SOC estimation, which can suppress the disturbance caused by unknown model errors. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the designed SOC observer combined with robust recursive least-squares-based model identification.

Enhanced Position Sensorless Control Using Bilinear Recursive Least Squares Adaptive Filter for Interior Permanent Magnet Synchronous Motor

Abstract: In the back electromotive force (EMF)-based sensorless control of interior permanent magnet synchronous motor (IPMSM), the inverter nonlinearity and flux linkage spatial harmonics will possibly give rise to (6 k ± 1)th harmonics in the estimated back EMF, especially the fifth and seventh harmonics. Those harmonics will consequently introduce (6 k )th harmonic ripples to the estimated rotor position, especially the sixth harmonic component. In order to solve this problem, a bilinear recursive least squares (BRLS) adaptive filter is proposed and integrated into a sliding-mode position observer to suppress the dominant harmonic components in the estimated back EMF and as a result, the accuracy of the estimated rotor position can be greatly improved. A unique feature of the BRLS adaptive filter is its ability to track and suppress the specified harmonic components in different steady state and dynamic operational conditions. The proposed method can compensate for harmonic ripples caused by the inverter nonlinearity and machine spatial harmonics at the same time; this method is also robust to machine parameter variation, and the BRLS algorithm itself is machine parameter independent. The implementation of the proposed BRLS filter in the sensorless control of IPMSM is explained in details in this paper. The enhanced drive performances using the BRLS filter have been thoroughly validated in different steady state and dynamic operational conditions on a 1.5-kW IPMSM sensorless drive.

Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems

Abstract: This paper presents an iterative learning approach for optimizing the course geometry in repetitive path following applications. In particular, we focus on airborne wind energy (AWE) systems. Our proposed algorithm consists of two key features. First, a recursive least squares (RLS) fit is used to construct an estimate of the behavior of the performance index. Second, an iteration-to-iteration path adaptation law is used to adjust the path shape in the direction of optimal performance. We propose two candidate update laws, both of which parallel the mathematical structure of common iterative learning control (ILC) update laws but replace the tracking-dependent terms with terms based on the performance index. We apply our formulation to the iterative crosswind path optimization of an AWE system, where the goal is to maximize the average power output over a figure-8 path. Using a physics-based AWE system model, we demonstrate that the proposed adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions under both constant and real wind profiles.

Model-Free Predictive Current Control of a Voltage Source Inverter

Abstract: Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.

DCD-Based Recursive Adaptive Algorithms Robust Against Impulsive Noise

Abstract: The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this brief, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formula. By employing different robust strategies against impulsive noise, we develop novel computationally efficient DCD-based robust recursive algorithms. Furthermore, to equip the proposed algorithms with the ability to track abrupt changes in unknown systems, a simple variable forgetting factor mechanism is also developed. Simulation results for channel identification scenarios in impulsive noise demonstrate the effectiveness of the proposed algorithms.

Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter

Abstract: The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi-Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values.

Robust hierarchical identification of Wiener systems in the presence of dynamic disturbances

Abstract: This paper is concerned with robust identification of Wiener systems in the presence of dynamic disturbances and stochastic noises. Since conventional statistical method cannot eliminate the dynamic disturbance. To solve this problem, the dynamic disturbance signal is modelled as a self-excitation time-varying parameters to be estimated by tracking strategy. Using the multi-innovation and auxiliary model scheme, a hierarchical recursive least squares algorithm with forgetting factors is designed in discrete-time domain. To improve the parameter estimation accuracy and decrease the error variance, the multi-innovation strategy is used to estimate the time-invariant system parameters. The dynamic disturbance still uses the single innovation method for quick tracking. The estimation error upper bound with finite sample data and asymptotic convergence properties are analyzed. Some guidelines are suggested to help the choice of multi-innovation length. The proposed parameterized identification facilitates controller design and system performance analysis. The effectiveness and superiority of the proposed algorithm are confirmed by utilizing theoretical analysis and numerical examples.

Recursive Least Squares with Variable-Direction Forgetting -- Compensating for the loss of persistency

Abstract: Learning depends on the ability to acquire and assimilate new information. This ability depends---somewhat counterintuitively---on the ability to forget. In particular, effective forgetting requires the ability to recognize and utilize new information to order to update a system model. This article is a tutorial on forgetting within the context of recursive least squares (RLS). To do this, RLS is first presented in its classical form, which employs uniform-direction forgetting. Next, examples are given to motivate the need for variable-direction forgetting, especially in cases where the excitation is not persistent. Some of these results are well known, whereas others complement the prior literature. The goal is to provide a self-contained tutorial of the main ideas and techniques for students and researchers whose research may benefit from variable-direction forgetting.

Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

Abstract: The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.

A New Variable Forgetting Factor-Based Bias-Compensation Algorithm for Recursive Identification of Time-Varying Multi-Input Single-Output Systems With Measurement Noise

Abstract: This article proposes a new variable forgetting factor (VFF) bias-compensated recursive least-squares (BCRLS) algorithm for the recursive identification of complex time-varying multi-input single-output (MISO) systems with measurement noise. It extends a previously developed real-valued BCRLS algorithm to complex signals and introduces new self-calibrated VFF and noise variance estimation schemes for tracking time-varying systems. The proposed VFF scheme offers faster tracking speed, especially for sudden system changes, while achieving a low steady-state (SS) mean square error (MSE) in a stationary environment. Moreover, the mean and mean square deviation of the complex RLS algorithm under zero-mean white Gaussian output additive noise are performed, from which the variance of the additive noise can be estimated. To mitigate the effect of finite-sample number, a self-calibration scheme is proposed to refine the FF at the SS and hence MSE. Simulations show that the proposed self-calibrated VFF-BCRLS algorithm offers improved tracking speed in sudden system changes and offers smaller MSE over the conventional BCRLS algorithm. Applications to real-world data for pH value prediction of a pH neutralization process and temperature prediction of a glass furnace also demonstrate the effectiveness of the proposed algorithm. The good performance and efficient implementation make it an attractive alternative to other conventional methods for system identification in control and optimization processes and other possible applications.

Hierarchical least squares identification for feedback nonlinear equation-error systems

Abstract: Because of complex structures, the identification of nonlinear systems is very difficult, especially for closed-loop nonlinear systems (i.e., feedback nonlinear systems). This paper considers the parameter identification of a feedback nonlinear system where the forward channel is a controlled autoregressive model and the feedback channel is a static nonlinear function. Using the hierarchical identification principle decomposes a feedback nonlinear system into two subsystems, one contains the parameters of the linear dynamic block and the other contains the parameters of the nonlinear static block. A hierarchical least squares algorithm and a recursive least squares algorithm are presented for feedback nonlinear systems. The proposed algorithms are simple in principle and easy to implement on-line.

Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm

Abstract: Accurate parameter identification of a lithium-ion battery is a critical basis in the battery management systems. Based on the analysis of the second-order RC equivalent circuit model, the parameter identification process using the recursive least squares (RLS) algorithm is discussed firstly. The reason for the RLS algorithm affecting the accuracy and rapidity of model parameter identification is pointed out. And an improved RLS algorithm is proposed, an inner loop with the estimated parameter vector updated multiple times is inserted into the conventional RLS algorithm, so that the identification results are improved. The test platform of a single lithium-ion battery is built. The experimental results show that the improved RLS algorithm has better tracking ability, smaller prediction error and has a moderate computational burden compared with the conventional RLS algorithm and a variable forgetting factor RLS algorithm.

A New Variable Forgetting Factor-Based Bias-Compensated RLS Algorithm for Identification of FIR Systems With Input Noise and Its Hardware Implementation

Abstract: This paper proposes a new variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QRRLS-BC) for system identification under input noise. A new variable forgetting factor scheme is proposed to improve its convergence speed and steady-state mean squares error. A new method for recursive estimation of the additive noise variance is also proposed for reliable bias compensation. The mean and mean-square asymptotic behaviors of the algorithm are analyzed and a self-calibration scheme is further proposed to improve the steady-state mean squares error (MSE) due to finite sample effect. Simulations show that the proposed VFF approach offers improved tracking and steady-state MSE performance over the conventional recursive least squares method and its fixed FF counterpart. A linear array architecture is proposed for the realization of this algorithm and several hardware efficient techniques are introduced to avoid the expensive cubic root and division operations required. The proposed algorithm is validated on Xilinx Zynq®-7000 AP SoC ZC702 Field Programmable Gate Array (FPGA). For a 10-tap finite impulse response (FIR) system, the implementation requires only about 11.5k slice look-up table (LUT)s, 4.5k slice registers and 50 DSP48s and it can work up to about 0.58 MHz sample rate with a 200 MHz system clock. The hardware resources are considerably lower than traditional techniques using divider and cubic root realization. The linear array architecture also serves as an attractive alternative to the systolic array in medium to low rate applications due to its reduced hardware usages.

Efficient QR Updating Factorization for Sensorless Synchronous Motor Drive Based on High Frequency Voltage Injection

Abstract: The conventional methods for estimating the rotor position of permanent magnet synchronous machines, at low speed range and characterized by rotor saliency, rely on high frequency voltage injection in the stator windings. Ordinarily, the rotor position estimation is achieved through the demodulation of the high frequency current response. In this article, an alternative method is presented for detecting rotor position from the rotating high frequency injection current response. The proposed ellipse fitting technique is not affected by signal processing delay effects and it requires the tuning of only one parameter, called forgetting factor, making the studied method suitable for industrial application thanks to its minimal setup effort. The inverse problem related to the ellipse fitting is solved implementing a QR recursive least squares algorithm. Efficient updating QR factorization has been adopted because of its features in terms of numerical stability and required limited computational effort. The proposed sensorless control scheme is validated by means of many experiments.

Multi-Head CNN-LSTM with Prediction Error Analysis for Remaining Useful Life Prediction

Abstract: Predicting accurate remaining useful life (RUL) of components plays a crucial role in making optimal decision for maintenance management. As sensor technology develops, multiple sensors are used to collect information for monitoring the condition of components. Deep learning architectures, such as convolutional neural network (CNN) and long short term memory (LSTM), can be considered as a successful end-to-end framework to predict RUL from the multivariate time series collected by those sensors. For that, we employ an architecture combining the parallel branch of CNN in series with LSTM which is referred to as multi-head CNN-LSTM. Furthermore, we propose a combination of the network with time series prediction error analysis (PEA). The prediction errors on the entire time series are estimated by recursive least squares (RLS) and single exponential smoothing (SES) respectively. We analyze each of the two sequences of prediction errors with the exponentially weighted moving average (EWMA) and combine them with the Fisher’s method. Finally, the output of the PEA is fed into the multi-head CNN-LSTM network as the additional input. We evaluate the performance of our method on the widely used C-MAPSS dataset. The experimental results suggest that using the PEA improves the performance of the deep learning-based RUL prediction model. Compared to other methods in recent literature, the proposed method achieves the state-of-the-art result on one sub-dataset and very competitive results on the others. In addition, it also shows promising results in the consecutive RUL prediction following the degradation process of components.

Robust De-Noising Technique for Accurate Heart Rate Estimation Using Wrist-Type PPG Signals

Abstract: Heart rate (HR) estimated using the photoplethysmography (PPG) signals during intense physical exercise is highly onerous owing to the existence of noise components like motion artifacts (MA’s) in the PPG signal. In this work, a robust de-noising technique for accurate HR estimation from the corrupted PPG signal is reported. The de-noising technique reported employs three pairs of Recursive Least Squares (RLS) as well as Normalized Least Mean Squares (NLMS) adaptive filters. The de-noised PPG signal obtained at the output of RLS and NLMS adaptive filters are combined using the sigmoid function. The three MA reduced PPG signals obtained from each adaptive filter pair are again combined using softmax activation function to form a MA reduced PPG signal from which the HR is estimated. Fast Fourier Transform (FFT) is used to estimate the HR and phase vocoder is used to refine the estimated HR. The proposed method is tested on the publically available 23 PPG datasets and it resulted in an HR estimation error of 1.89 beat per minute (BPM). The HR estimated using the proposed technique is found to be accurate and the HR estimation error is 1.89 BPM which is less when compared to other existing HR estimation mechanisms that reported an error of 1.97 BPM and 2.09 BPM.

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography.

Abstract: This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.