System Parameter Estimation Research Articles

On the Asymptotic Behavior and Parameter Estimation of a Double-Sided LCC-Compensated Wireless Power Transfer System

This study investigates the statistic behavior and parameter estimation problems of a double-sided, LCC-compensated, wireless power transfer system. Based on the commonly used wireless charging circuit model, this study proposes a five-step parameter estimation method, which is applicable to automotive static wireless charging systems. The eight parameters in the circuit model of this study are the most important key components of the wireless charging system. The study also found that, under certain conditions, the statistic mode of wireless charging systems has a specific distribution. However, the current status of these eight components for wireless charging of electric vehicles will have complex parameter drift problems. These drift problems will deteriorate the performance of the vehicle power systems. This study probes these factors and proposes some related mathematical theories. The noted factors can be applied to the analysis of the wireless charging system and provide alternative solutions to explain the deteriorations from coil misalignments. Both simulations and experiments are given to show the evaluated issues of the proposed study.

Optimized State of Charge Estimation of Lithium-Ion Battery in SMES/Battery Hybrid Energy Storage System for Electric Vehicles

With the increasing capacity of large-scale electric vehicles, it's necessary to stabilize the fluctuation of charging voltage in order to achieve improvement of lithium-ion battery lifecycle, and the hybrid energy storage system (HESS) including superconducting magnetic energy storage (SMES) and lithium-ion battery is introduced, which is significant to reduce the high frequency discharge cycles and to manage the state of charge (SOC) for batteries with an optimized integration scheme based on power grading approach. With the unscented Kalman filter (UKF) method for battery SOC estimation and the extended Kalman filter (EKF) method for battery internal resistance estimation, the approach for estimating the lithium-ion battery state through adaptive unscented Kalman filter (AUKF) is derived. The AUKF approach takes the form of iterative loop to estimate system parameters and state, so the system strategy has well adaptive characteristics. Experimental results illustrate that the SOC estimation based on the proposed optimized approach for lithium-ion battery in HESS can achieve the well robustness and fast convergence.

Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach.

In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of "how well do we know our parameters, along with their uncertainties?." The results reveal that Bayesian inversion statistical analysis on an example, prototype nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. The paper can also be seen as a tutorial with the additional goal of achieving a "Gold Standard" Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.

Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

How to Deal with Parameter Estimation in Continuous-Time Stochastic Systems

How to Deal with Parameter Estimation in Continuous-Time Stochastic Systems

Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise

The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the gap between neuroscience and robotics by reformulating an FEP-based inference scheme—Dynamic Expectation Maximization—into an algorithm that can perform simultaneous state, input, parameter, and noise hyperparameter estimation of any stable linear state space system subjected to colored noises. The resulting estimator was proved to be of the form of an augmented coupled linear estimator. Using this mathematical formulation, we proved that the estimation steps have theoretical guarantees of convergence. The algorithm was rigorously tested in simulation on a wide variety of linear systems with colored noises. The paper concludes by demonstrating the superior performance of DEM for parameter estimation under colored noise in simulation, when compared to the state-of-the-art estimators like Sub Space method, Prediction Error Minimization (PEM), and Expectation Maximization (EM) algorithm. These results contribute to the applicability of DEM as a robust learning algorithm for safe robotic applications.

System Calibration Including Time Delay Estimation for GNSS/INS-Assisted Pushbroom Scanners Onboard UAV Platforms

Unmanned aerial vehicles (UAVs ) equipped with imaging sensors and integrated global navigation satellite system/inertial navigation system (GNSS/INS ) units are used for numerous applications. Deriving reliable 3D coordinates from such UAVs is contingent on accurate geometric calibration, which encompasses the estimation of mounting parameters and synchronization errors. Through a rigorous impact analysis of such systematic errors, this article proposes a direct approach for spatial and temporal calibration (estimating system parameters through a bundle adjustment procedure) of a GNSS/INS -assisted pushbroom scanner onboard a UAV platform. The calibration results show that the horizontal and vertical accuracies are within the ground sampling distance of the sensor. Unlike for frame camera systems, this article also shows that the indirect approach is not a feasible solution for pushbroom scanners due to their limited ability for decoupling system parameters. This finding provides further support that the direct approach is recommended for spatial and temporal calibration of UAV pushbroom scanner systems.

Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports

Characterising intractable high-dimensional random variables is one of the fundamental challenges in stochastic computation. The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables. This paper generalises the functional tensor-train approximation of the inverse Rosenblatt transport recently developed by Dolgov et al. (Stat Comput 30:603–625, 2020) to a wide class of high-dimensional non-negative functions, such as unnormalised probability density functions. First, we extend the inverse Rosenblatt transform to enable the transport to general reference measures other than the uniform measure. We develop an efficient procedure to compute this transport from a squared tensor-train decomposition which preserves the monotonicity. More crucially, we integrate the proposed order-preserving functional tensor-train transport into a nested variable transformation framework inspired by the layered structure of deep neural networks. The resulting deep inverse Rosenblatt transport significantly expands the capability of tensor approximations and transport maps to random variables with complicated nonlinear interactions and concentrated density functions. We demonstrate the efficiency of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.

Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

Continuous-variable quantum key distribution (CVQKD) allows legitimate parties to extract and exchange secret keys. However, the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system. In this paper, we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks (ANN), which can be merged in post-processing with less additional devices. The ANN-based training scheme, enables key prediction without exposing any raw key. Experimental results show that the error between the predicted values and the true ones is in a reasonable range. The CVQKD system can be improved in terms of the secret key rate and the parameter estimation, which involves less additional devices than the traditional CVQKD system.

Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

Defining the optimal parameters for the photovoltaic (PV) system model is essential for the design, evolution, development, estimation, and analysis of the PV power system. Therefore, it is crucial to properly identify the best parameters of the PV models based on modern computational techniques. As a result, this research proposes a new Orthogonal-Learning-Based Gray Wolf Optimizer (OLBGWO) for identifying uncertain parameters in PV cell models using a local exploratory approach. The orthogonal-learning-based (OLB) technique enhances the exploitation and exploration capabilities of the original Gray Wolf Optimizer (GWO) and modified vector parameter called a, which promotes a highly reliable balance between the exploitation and exploration phases of the algorithm. During the iterative procedure of OLBGWO, the OLB methodology is employed to obtain the optimal solution for the weaker populations and guides the population to examine the prospective search area. Additionally, in OLBGWO, an exponential decay function is used to reduce the value of vector a. The proposed approach is used to solve the PV system's parameter estimation problem. The presented OLBGWO algorithm estimates the uncertain parameters of the single-diode model (SDM), double-diode model (DDM), and PV module model. The OLBGWO's performance is compared to those of other competing algorithms to demonstrate its superiority. The simulation results demonstrate that the OLBGWO algorithm provides fast convergence speed while maintaining high solution accuracy.

Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation, and Optimal Measurement Placement in Power Systems?

We consider a power transmission system monitored using phasor measurement units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically the pre-fault state and the (not cleared) post-fault state, are recorded by the PMUs and are available for training, we first design a comprehensive sequence of neural networks (NNs) locating the faulty line. Performance of different NNs in the sequence, including linear regression, feed-forward NNs, AlexNet, graph convolutional NNs, neural linear ordinary differential equations (ODEs) and neural graph-based ODEs, ordered according to the type and amount of the power flow physics involved, are compared for different levels of observability. Second, we build a sequence of advanced power system dynamics–informed and neural ODE–based machine learning schemes that are trained, given the pre-fault state, to predict the post-fault state and also, in parallel, to estimate system parameters. Finally, third and continuing to work with the first (fault localization) setting, we design an (NN-based) algorithm which discovers optimal PMU placement.

Parameter Estimation of Partially Observed Turbulent Systems Using Conditional Gaussian Path-Wise Sampler

Parameter estimation of complex nonlinear turbulent dynamical systems using only partially observed time series is a challenging topic. The nonlinearity and partial observations often impede using closed analytic formulae to recover the model parameters. In this paper, an exact path-wise sampling method is developed, which is incorporated into a Bayesian Markov chain Monte Carlo (MCMC) algorithm in light of data augmentation to efficiently estimate the parameters in a rich class of nonlinear and non-Gaussian turbulent systems using partial observations. This path-wise sampling method exploits closed analytic formulae to sample the trajectories of the unobserved variables, which avoid the numerical errors in the general sampling approaches and significantly increase the overall parameter estimation efficiency. The unknown parameters and the missing trajectories are estimated in an alternating fashion in an adaptive MCMC iteration algorithm with rapid convergence. It is shown based on the noisy Lorenz 63 model and a stochastically coupled FitzHugh–Nagumo model that the new algorithm is very skillful in estimating the parameters in highly nonlinear turbulent models. The model with the estimated parameters succeeds in recovering the nonlinear and non-Gaussian features of the truth, including capturing the intermittency and extreme events, in both test examples.

State and parameter estimation of stochastic physical systems from uncertain and indirect measurements

State and parameter estimation of stochastic physical systems from uncertain and indirect measurements

Preface

The 2021 International Conference on Information Technology and Intelligent Control (CITIC 2021) was held on 23-25 July 2021 as a virtual conference due to the growing concerns over the coronavirus outbreak (COVID-19), and in order to protect the well-being of our attendees, partners, and staff as our number one priority. With the rapid progress of informatization globally, the world's demand for information multiplies, and information technology has become the cornerstone of economic activities and social life. With the development of information technology, intelligent control methods and technologies have gradually updated and grown. Intelligent control has rapidly moved to various professional fields and applied to different complex controlled objects' control problems.CITIC 2021 is an international forum for scientists, engineers, and practitioners to present their latest research and development results in all areas of Information Technology and Intelligent Control. Conference topics mainly include but are not limited to the Application of Information Technology; Artificial Intelligence; Programming Languages; Digital Devices and Computer Components; Health and Medical Science and Technology; GIS; Data Mining and Data Warehousing; Intelligent system. CITIC 2021 welcomes all high-quality research papers and presentations from related research fields.This scientific event brings together more than 200 national and international researchers in Information Technology and Intelligent Control. On top of the local participants coming from different national universities, international participants are also registered from different countries, namely Singapore, Australia, and USA. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.In the second part, we invited five professors as our keynote speakers. Prof. Hui Bi, Nanjing University of Aeronautics and Astronautics (NUAA), China performed a speech: Sparse SAR Imaging: From Theory to Practical Application. And then we had Prof. Weinan Gao, from Florida Institute of Technology, USA. He presented an insightful speech: Learning-Based Adaptive Optimal Control and Applications to Connected and Autonomous Vehicles. Prof. Bingkun Bao, from Nanjing University of Posts and Telecommunications. She delivered a wonderful speech: Analysis and Perception of Multimedia Data. Prof. Farhad Shahnia, from Murdoch University, Australia, performed a speech: Recent and Future Research on Microgrid Clusters. Prof. Zhihong Man, as our finale keynote speaker, from Swinburne University of Technology, Australia. He delivered a wonderful speech: A new theoretical framework of parameter estimation of linear systems. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Aerospace Technology, Automation technology and equipment, Data Mining and Data Warehousing, Information Systems, Robotics and more related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to acknowledge all of those who supported CITIC 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers.List of titles Committee member, TPC Chair, Publication Chair, Organizing Committee, International Technical Program Committee are available in this Pdf.

FPGA Implementation of Modified Reconfigurable Adaptive Gain Scheduling Controller

This article aims to put forward a modified type of adaptive gain scheduling that will be able to deal with the immeasurable and unpredictable variations of system variables by adapting its value at each time instance to follow any change in the input and overcome any disturbance applied to the system without the need to predetermine gains values. In addition, the inverse neural controller will precede the gain scheduling to eliminate the need for complex system linear zing and parameter estimation. Therefore, the problems of needing complex mathematics for system linearization and gains calculations have been solved. The performance of the presented controller was tested by comparing the step response of a DC-motor controlled via the proposed technique and the response of that motor when controlled by the inverse neural controller and PID controller. MATLAB/Simulink has been used for making the simulations and obtaining the results. In addition, the FPGA implementation of the proposed controller has been presented. The results showed a remarkable improvement in the transient response of the system for all of the rising time, delay time, settling time, peak overshoot, and steady-state error.

Variable leaky steepest descent algorithm for autoregressive fading estimation in OFDM systems

This paper presents an adaptive algorithm for unbiased estimation of autoregressive Rayleigh flat fading parameters in OFDM systems. To this end, a variable leaky steepest descent algorithm is proposed. The authors use a variable leak parameter in the algorithm to remove the noise induced bias from the autoregressive estimates. Theoretical performance analyses of the proposed method are conducted. Moreover, computer simulations are presented to evaluate the performance of the proposed algorithm and to compare it with other existing methods.

Haptic control of steer-by-wire systems using parameter estimation of rack system lateral load model

This paper describes a haptic control of steer-by-wire systems for rendering the conventional steering system under various road conditions using parameter estimation of rack system lateral load model. To design the conventional steering system model, the dynamic model of a rack system has been developed as a mass-spring-damper system with a friction model. An online parameter estimation of the rack system was designed to consider the various road conditions in the haptic rendering. In a steering wheel system, which is a haptic device, a limit cycle, and instable behavior can occur due to the sampling rate and quantization. To prevent the limit cycle and instable behavior, a passivity analysis was conducted and constraint conditions of the rendering coefficient was derived. The haptic control algorithm is designed to render the conventional steering system without limit cycles using passivity conditions. The performance of the proposed controller was evaluated via both computer simulations and vehicle tests under various steering conditions. The results demonstrate that the proposed algorithm ensures haptic rendering performance on dry and wet asphalt conditions.

An efficient identification approach for highly complex non-linear systems using the evolutionary computing method based Kalman filter

This paper proposes a new and efficient approach where a nature-inspired optimisation technique has abetted the Kalman filter (KF) for accurately solving the parametric estimation problem of highly complex non-linear systems. The KF is the best optimal state estimator in terms of normalised mean squared error (NMSE) for linear Gaussian state-space models. However, the use of mismatched noise statistics in KF might result in performance degradation. To address this issue, three steps are proposed in this work for the accurate estimation of the unknown non-linear system parameters by using the Volterra model. The first step is to reformulate the Volterra model into a measurement form. Secondly, the KF parameters are optimised by using an evolutionary algorithm with an efficient objective function. The third step is to estimate the coefficients of the unknown system by using the KF technique with the help of optimally tuned KF parameters achieved in the second step. In simulations, three distinct higher memory size second-order Volterra models, two non-linear benchmark systems and the primary path of active-noise control (ANC) system based on real data sets are identified by using the basic KF, genetic algorithm (GA) assisted KF (GA-KF), particle swarm optimisation (PSO) assisted KF (PSO-KF), firefly algorithm (FA) assisted KF (FA-KF) and ant lion optimisation (ALO) assisted KF (ALO-KF) techniques. The experimental results illustrate that the ALO-KF approach leads to better coefficient estimation compared to FA-KF, PSO-KF, GA-KF, and basic KF methods.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

In this study a novel cooperative controller design is developed to tackle the consensus tracking problem of Multi-Agent systems (MAS) based on multilayer perceptron neural network (MLPNN), applied on a distributed synchronous generator (SG) Multi-Agent system in presence of model uncertainties and external disturbances. Application of MLPNN in controller design can lead to smoother system response and can neutralize the impacts of model uncertainties. Furthermore, the proposed method benefits from a novel algorithm formerly known as error backpropagation (BP) algorithm to update and to regulate the weights of MLPNN adaptively based on the principles of consensus error. The proposed strategy can be very effective in control of the distributed SG Multi-Agent system due to its ability for system identification, parameter estimation, and disturbance approximation. Moreover, the utilization of neural networks can meet the criterion to make the consensus error uniformly ultimately bounded. Ultimately, simulation results illustrate the applicability and effectiveness of the novel MLPNN controller to model the system uncertainties and to deal with external disturbances of the distributed SG Multi-Agent system.

Parameter estimation for a chaotic dynamical system with partial observations

Abstract Parameter estimation in chaotic dynamical systems is an important and practical issue. Nevertheless, the high-dimensionality and the sensitive dependence on initial conditions typically makes the problem difficult to solve. In this paper, we propose an innovative parameter estimation approach, utilizing numerical differentiation for observation data preprocessing. Given plenty of noisy observations on a portion of dependent variables, numerical differentiation allows them and their derivatives to be accurately approximated. Substituting those approximations into the original system can effectively simplify the parameter estimation problem. As an example, we consider parameter estimation in the well-known Lorenz model given partial noisy observations. According to the Lorenz equations, the estimated parameters can be simply given by least squares regression using the approximated functions provided by data preprocessing. Numerical examples show the effectiveness and accuracy of our method. We also prove the uniqueness and stability of the solution.