scispace - formally typeset
Search or ask a question

Showing papers on "Recursive least squares filter published in 2017"


Journal ArticleDOI
TL;DR: In this article, a multi-timescale method for dual estimation of state of charge (SOC) and capacity with an online identified battery model is presented, where the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them.

235 citations


Journal ArticleDOI
Ling Xu1, Feng Ding1
TL;DR: This paper studies the parameter estimation problem for the sine combination signals and periodic signals and presents the multi-innovation stochastic gradient parameter estimation method, derived by means of the trigonometric function expansion.
Abstract: The sine signals are widely used in signal processing, communication technology, system performance analysis and system identification. Many periodic signals can be transformed into the sum of different harmonic sine signals by using the Fourier expansion. This paper studies the parameter estimation problem for the sine combination signals and periodic signals. In order to perform the online parameter estimation, the stochastic gradient algorithm is derived according to the gradient optimization principle. On this basis, the multi-innovation stochastic gradient parameter estimation method is presented by expanding the scalar innovation into the innovation vector for the aim of improving the estimation accuracy. Moreover, in order to enhance the stabilization of the parameter estimation method, the recursive least squares algorithm is derived by means of the trigonometric function expansion. Finally, some simulation examples are provided to show and compare the performance of the proposed approaches.

140 citations


Journal ArticleDOI
TL;DR: This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems) by using the gradient search and proposes a state observer-based stochastic gradient algorithm and three algorithms derived by means of the multi-innovation theory.
Abstract: This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms.

121 citations


Journal ArticleDOI
TL;DR: In this paper, a recursive least squares (RLS) estimation method based on the auxiliary model identification idea and the decomposition technique is presented for pseudo-linear system identification with missing data, and an interval-varying RLS algorithm is derived for estimating the system parameters.
Abstract: This study focuses on the parameter identification problems of pseudo-linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS algorithm is presented. Then for the system identification with missing data, an interval-varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval-varying RLS algorithm and introduces the forgetting factors to track the time-varying parameters. The simulation results show that the proposed algorithms can work well.

112 citations


Journal ArticleDOI
21 Dec 2017-Energies
TL;DR: In this article, two closed-loop state of charge (SOC) estimation algorithms with online parameter identification are proposed to solve the problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter.
Abstract: State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

84 citations


Journal ArticleDOI
TL;DR: The Akiake Information Criterion and its related model comparison indices are explained in the context of least squares (ordinary, weighted, iterative weighted or “generalized”, etc.) based inverse problem formulations.

81 citations


Journal ArticleDOI
TL;DR: It is shown that the sensitivity to node layout is removed and that conditioning can be controlled through oversampling, and the least squares formulation is shown to be suitable for collocation-based radial basis function-PUMs.
Abstract: Recently, collocation-based radial basis function (RBF) partition of unity methods (PUMs) for solving partial differential equations have been formulated and investigated numerically and theoretically. When combined with stable evaluation methods such as the RBF-QR method, high order convergence rates can be achieved and sustained under refinement. However, some numerical issues remain. The method is sensitive to the node layout, and condition numbers increase with the refinement level. Here, we propose a modified formulation based on least squares approximation. We show that the sensitivity to node layout is removed and that conditioning can be controlled through oversampling. We derive theoretical error estimates both for the collocation and least squares RBF-PUMs. Numerical experiments are performed for the Poisson equation in two and three space dimensions for regular and irregular geometries. The convergence experiments confirm the theoretical estimates, and the least squares formulation is shown to ...

77 citations


Journal ArticleDOI
TL;DR: In this article, an online adaptive battery model is proposed to reproduce the vanadium redox battery dynamics accurately, and the model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF).

76 citations


Journal ArticleDOI
TL;DR: The parameter estimation problem for multi-input multi-output Hammerstein systems is considered and the modified Kalman filter algorithm is derived to estimate the unknown intermediate variables in the system and the MKF-based recursive least squares algorithm is presented to estimate all the unknown parameters.
Abstract: The parameter estimation problem for multi-input multi-output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time-invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm is derived to estimate the unknown intermediate variables in the system and the MKF-based recursive least squares (LS) algorithm is presented to estimate all the unknown parameters. Furthermore, the hierarchical identification is adopted to decompose the system into two fictitious subsystems: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear subsystem. Then an MKF-based hierarchical LS algorithm is derived. The convergence analysis shows the performance of the presented algorithms. The numerical simulation results indicate that the proposed algorithms are effective.

73 citations


Journal ArticleDOI
TL;DR: In this paper, a cost-effective observers are designed based on an adaptive scheme and a recursive least squares algorithm without the addition of extra sensors on a production vehicle or modification of the vehicle control system.
Abstract: It is well known that both the tire-road friction coefficient and the absolute vehicle velocity are crucial factors for vehicle safety control systems Therefore, numerous efforts have been made to resolve these problems, but none have presented satisfactory results in all cases In this paper, cost-effective observers are designed based on an adaptive scheme and a recursive least squares algorithm without the addition of extra sensors on a production vehicle or modification of the vehicle control system This paper has three major contributions First, the front biased braking characteristics of production vehicles such that the front wheel brake torques are saturated first are exploited when estimating the tire-road friction coefficient Second, the vehicle absolute speed is identified during the friction coefficient estimation process Third, unlike the conventional method, this paper proposes using already available excitation signals in production vehicles In order to verify the performance of the proposed observers, experiments based on real production vehicles are conducted, and the results reveal that the proposed algorithm can enhance the performance of any vehicle dynamics control systems

67 citations


Journal ArticleDOI
TL;DR: An online identification algorithm is presented for nonlinear systems in the presence of output colored noise based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form.
Abstract: In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.

Journal ArticleDOI
TL;DR: A data filtering based recursive least squares algorithm is proposed based on the data filtering technique and results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized most squares algorithm.
Abstract: Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.

Journal ArticleDOI
17 Feb 2017-PLOS ONE
TL;DR: The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.
Abstract: Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries.

Journal ArticleDOI
TL;DR: The proposed heart rate estimation scheme offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.

Journal ArticleDOI
TL;DR: An improved QRS detection algorithm, based on adaptive filtering principle, has been designed and performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time.
Abstract: Electrocardiogram (ECG) is one of the most important physiological signals of human body, which contains important clinical information about the heart. Monitoring of ECG signal is done through QRS detection. In this paper, an improved QRS detection algorithm, based on adaptive filtering principle, has been designed. Enumeration of the effectiveness of various LMS variants used in adaptive filtering based QRS detection algorithm has been done through fidelity parameters like sensitivity and positive predictivity. Whole family of LMS algorithm has been implemented for comparison. Sign-sign LMS, sign error LMS, basic LMS and normalized LMS are re-implemented, while variable leaky LMS, variable step-size LMS, leaky LMS, recursive least squares (RLS), and fractional LMS are novel combination presented in this paper. After analysis of the obtained results, performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time of 99.68%, 99.84%, and 0.45 s respectively. Reported results are tested and evaluated over MIT/BIH arrhythmia database. Presented study also concludes that the performance of most of the variants gets affected due to low SNR but the Leaky LMS performs better even under heavy noise conditions.

Journal Article
TL;DR: Simulation results show that HS-ESN is significantly the fastest algorithm for training ESN whereas can effectively meet the requirements of the output precision, and HS-RLS-ESn algorithm firstly uses HS to close to solution region then it uses RLS to obtain less error.
Abstract: Echo State Networks (ESN) are a special form of recurrent neural networks (RNNs), which allow for the black box modeling of nonlinear dynamical systems. A unique feature of an ESN is that a large number of neurons (the “reservoir”), whose synaptic connections are generated randomly, is used in such that only the connections from the reservoir to the output modified by learning. The computation of optimal weights can then be achieved by a simple linear regression in an offline manner. ESNs have been applied to a variety of tasks from time series prediction to dynamic pattern recognition with great success. In many tasks, however, an online adaptive learning of the output weights is required. Harmony Search (HS) algorithm shows good performance when the search space is large. Here we propose HS algorithm for training echo state network in an online manner. In our simulation experiments, the ESNs are trained for predicting of three different time series including Mackey-Glass, Lorenz chaotic and Rossler chaotic time series with four different algorithms including Recursive Least Squares (RLS-ESN), Particle Swarm Optimization (PSO-ESN), and our proposed methods (HS-ESN and HS-RLS-ESN). Simulation results show that HS-ESN is significantly the fastest algorithm for training ESN whereas can effectively meet the requirements of the output precision. HS-RLS-ESN algorithm firstly uses HS to close to solution region then it uses RLS to obtain less error. HS-RLS-ESN is slower than HS-ESN and faster than RLS-ESN, but its generality power is very close to RLS-ESN.

Journal ArticleDOI
TL;DR: By using a probabilistic interpretation, this work presents a novel similarity measure between two complex random variables, which is defined as complex correntropy, which can be applied to solve several problems involving complex data in a more straightforward way.
Abstract: Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher order statistical moments in non-Gaussian noise environments. Although correntropy has been used with complex data, no theoretical study was pursued to elucidate its properties, nor how to best use it for optimization. By using a probabilistic interpretation, this work presents a novel similarity measure between two complex random variables, which is defined as complex correntropy. A new recursive solution for the maximum complex correntropy criterion is introduced based on a fixed-point solution. This technique is applied to a system identification, and the results demonstrate prominent advantages when compared against three other algorithms: the complex least mean square, complex recursive least squares, and least absolute deviation. By the aforementioned probabilistic interpretation, correntropy can now be applied to solve several problems involving complex data in a more straightforward way.

Journal ArticleDOI
TL;DR: The utility of an adaptive method of cancellation of parasitic vibrations for embedded vibration sensing corrupted by extraneous parasitic motion is demonstrated as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor.
Abstract: In this paper, an adaptive method of cancellation of parasitic vibrations is presented for a self-mixing (SM) interferometric laser vibration sensor that has been coupled with a solid-state accelerometer (SSA). Previously, this was achieved using a precalibration of phase and gain mismatches over the complete bandwidth of the instrument. Such a precalibration is not only tedious to execute but also hinders a mass production of the instrument as every SSA–SM sensor couple requires customized calibration. On the other hand, the proposed method does not require any precalibration as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor. Two different adaptive algorithms, namely, recursive least squares (RLS) and least mean squares (LMS) algorithms, are tested and a comparison is established on the basis of parameter dependence, convergence time, computational cost, and rms error. The proposed algorithms have provided improved results (mean errors of 19.1 nm and 20.2 nm for LMS and RLS, respectively) compared with the precalibration-based results (mean error of 24.7 nm) for a laser wavelength of 785 nm. The simulated and experimental results thus demonstrate the utility of such an approach for embedded vibration sensing corrupted by extraneous parasitic motion.

Journal ArticleDOI
TL;DR: In this article, a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system is presented, and a modified identification equation incorporating the contact force, as well as the force/torque of the end effector, is proposed to weaken the accumulation of errors and improve the identification accuracy.

Journal ArticleDOI
TL;DR: This work proposes Adaptation (based on the Recursive Least Squares (RLS) algorithm), for online system identification to take changes in operating points into account when computing the DMC control action.

Proceedings ArticleDOI
24 Jul 2017
TL;DR: The design and implementation of adaptive PID control strategy for controlling the angular velocity of the DC motor is described and the proportional, integral and derivative constants of controller can be obtained by using online pole placement method.
Abstract: This paper describes the design and implementation of adaptive PID control strategy for controlling the angular velocity of the DC motor. Adaptive PID controller is designed to calculate the control parameters which are tuned adaptively to give desired control performance even if parameters of DC Motor are changed. The controller's parameters are online tuned when the motor is running using a Recursive Least Squares (RLS) method. The controller is able to change the value of the controller's constants to maintain motor performance as it is desired when parameters of DC motor are changed. Initially a Pseudo Random Binary Sequence (PRBS) signal is given to the system for 0.07 seconds to get the estimated transfer function of the plant system (DC motor) using the RLS algorithm. From coefficients of the estimated system's transfer function, the poles of a desired characteristic equation can be obtained for the system that has the appropriate output. Thus, the proportional, integral and derivative constants of controller can be obtained by using online pole placement method. Here, an online identification system is used to determine the new control parameters. The effectiveness of this adaptive PID controller is verified by experimental results using a microcontroller STM32F446.

Journal ArticleDOI
TL;DR: The experimental results suggest that the proposed method outperforms popular MA reduction methods such as normalized least mean squares (NLMS) and recursive least squares (RLS) as well as independent component analysis (ICA).
Abstract: The focus of this paper is motion artifact (MA) reduction from the impedance pneumography (IP) signal, which is widely used to monitor respiration. The amplitude of the MA that contaminates the IP signal is often much larger than the amplitude of the respiratory component of the signal. Moreover, the morphology and frequency composition of the artifacts may be very similar to that of the respiration, making it difficult to remove these artifacts. The proposed filter uses a regularization term to ensure that the pattern of the filtered signal is similar to that of respiration. It also ensures that the amplitude of the filter output is within the expected range of the IP signal by imposing an $\varepsilon$ -tube on the filtered signal. The adaptive $\varepsilon$ -tube filter is 100 times faster than the previously proposed nonadaptive version and achieves higher accuracies. Moreover, the experimental results, using several different performance measures, suggest that the proposed method outperforms popular MA reduction methods such as normalized least mean squares (NLMS) and recursive least squares (RLS) as well as independent component analysis (ICA). When used to extract the respiratory rate, the adaptive $\varepsilon$ -tube achieves a mean error of 1.27 breaths per minute (BPM) compared to 4.72 and 4.63 BPM for the NLMS and RLS filters, respectively. When compared to the ICA algorithm, the proposed filter has an error of 1.06 BPM compared to 3.47 BPM for ICA. The statistical analyses indicate that all of the reported performance improvements are significant.

Journal ArticleDOI
TL;DR: An improved online model-based parameter identification algorithm, which requires less computational capability and storage space but performs worse than the RLS algorithm, and can maintain the maximum SoC estimation error at less than 10%.

Journal ArticleDOI
Dandan Meng1
TL;DR: This paper gives the input–output representation of the bilinear systems through eliminating the state variables in the model and derives a least squares algorithm and a multi-innovation stochastic gradient algorithm for identifying the parameters of bilInear systems based on the least squares principle and the multi- Innovation identification theory.
Abstract: Bilinear systems are a special class of nonlinear systems. Some systems can be described by using bilinear models. This paper considers the parameter identification problems of bilinear stochastic systems. The difficulty of identification is that the model structure of the bilinear systems includes the products of the states and inputs. To this point, this paper gives the input---output representation of the bilinear systems through eliminating the state variables in the model and derives a least squares algorithm and a multi-innovation stochastic gradient algorithm for identifying the parameters of bilinear systems based on the least squares principle and the multi-innovation identification theory. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.

Journal ArticleDOI
TL;DR: Two zero-attracting recursive least squares algorithms are derived by employing the inline-formula-norm of the parameter vector constraint to facilitate model sparsity to demonstrate the effectiveness of the proposed approach.
Abstract: The $l_{1}$ -norm sparsity constraint is a widely used technique for constructing sparse models. In this paper, two zero-attracting recursive least squares algorithms, which are referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the $l_{1}$ -norm of the parameter vector constraint to facilitate model sparsity. To achieve a closed-form solution, the $l_{1}$ -norm of the parameter vector is approximated by an adaptively weighted $l_{2}$ -norm in which the weighting factors are set as the inversion of the associated $l_{1}$ -norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra and the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

Journal ArticleDOI
TL;DR: A novel linear filter, called the state space maximum correntropy (SSMC) filter, which is derived under the maximum Correntropy criterion (MCC) instead of the MMSE, which performs very well in non-Gaussian noises especially when the signals are corrupted by impulsive noises.

Journal ArticleDOI
TL;DR: A semiblind spatial fractionally spaced equalizer that uses a novel space-time recursive least-square adaptive algorithm to counteracts the blur introduced by the optical channel, and numerical results show how the bit error rate can be drastically reduced in both motion and out-of-focus blur scenarios.
Abstract: This paper presents a novel space-time recursive least-squares adaptive algorithm, which performs filter coefficients updates in space and postponed filtering in time. The algorithm is used for intersymbol interference suppression in optical camera communications, which is a subgroup of visible light communication systems. Optical camera communications uses image sensor receivers, as those available in smartphones, tablets, and laptops, to detect changes in light intensity in order to allow data transmission. The achievable data transmission rate of optical camera communication systems is nowadays constrained by the frame-per-second rate achieved by those devices, so that the spatial dimension, e.g., multiple-input multiple-output techniques, are typically exploited. Spatial intersymbol interference could arise and image blurring can be an issue especially when the link distance grows and/or when the receiver is in mobility. We present here a semiblind spatial fractionally spaced equalizer that uses a novel space-time recursive least-square adaptive algorithm to counteracts the blur introduced by the optical channel. Numerical results show how the bit error rate can be drastically reduced in both motion and out-of-focus blur scenarios.

Journal ArticleDOI
TL;DR: In this paper, a comprehensive active heave compensation (AHC) approach is proposed, which consists of estimation, prediction and control methods, and the estimation concept covers the estimation of the attitude using sensor fusion and estimation of heave which is obtained by applying adaptive filtering methods.

Journal ArticleDOI
TL;DR: A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least Squares regression algorithm is proposed for high dimension multi-way (tensor) data treatment and adaptive modeling of complex processes in real-time.
Abstract: A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least Squares (REW-NPLS) regression algorithm is proposed for high dimension multi-way (tensor) data treatment and adaptive modeling of complex processes in real-time. The method unites fast and efficient calculation schemes of the Recursive Exponentially Weighted PLS with the robustness of tensor-based approaches. Moreover, contrary to other multi-way recursive algorithms, no loss of information occurs in the REW-NPLS. In addition, the Recursive-Validation method for recursive estimation of the hyper-parameters is proposed instead of conventional cross-validation procedure. The approach was then compared to state-of-the-art methods. The efficiency of the methods was tested in electrocorticography (ECoG) and magnetoencephalography (MEG) datasets. The algorithms are implemented in software suitable for real-time operation. Although the Brain-Computer Interface applications are used to demonstrate the methods, the proposed approaches could be efficiently used in a wide range of tasks beyond neuroscience uniting complex multi-modal data structures, adaptive modeling, and real-time computational requirements.

Journal ArticleDOI
TL;DR: A novel diffusion variable FF RLS (Diff-VFF-RLS) algorithm based on a local polynomial modeling of the unknown TV system and a new optimal VFF formula that tries to minimize the estimation deviation is obtained.