A Generalized Recursive Identification Algorithm Compensated by Orthogonal Weighted Kernel for Tracking Time-Variant Systems

[1]  Weilin Li,et al.  State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer , 2017, IEEE Transactions on Industrial Electronics.

[2]  Behrouz Safarinejadian,et al.  A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems , 2018, Circuits Syst. Signal Process..

[3]  Feng Ding,et al.  Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition , 2016, Circuits Syst. Signal Process..

[4]  Hugues Garnier,et al.  Direct continuous-time approaches to system identification. Overview and benefits for practical applications , 2015, Eur. J. Control.

[5]  Haiquan Zhao,et al.  Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks , 2018, IEEE Transactions on Signal Processing.

[6]  Javad Marzbanrad,et al.  Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade , 2016 .

[7]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[8]  J. Marzbanrad,et al.  Biomechanical Modeling of a Seated Human Body Exposed to Vertical and Horizontal Vibrations Using Genetic Algorithms , 2018, Journal of Vibration Engineering & Technologies.

[9]  A. Khaki-Sedigh,et al.  Adaptive control of nonlinear in parameters chaotic system via Lyapunov exponents placement , 2009 .

[10]  Hamid Khaloozadeh,et al.  A stable adaptive synchronization scheme for uncertain chaotic systems via observer , 2009 .

[11]  Mohammad R. Homaeinezhad,et al.  Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops , 2015 .

[12]  Michel Kinnaert,et al.  SOC and SOH estimation for Li-ion battery based on an equivalent hydraulic model. Part II: SOH power fade estimation , 2016, 2016 American Control Conference (ACC).

[13]  Frank Gauterin,et al.  Recursive Generalized Total Least Squares with Noise Covariance Estimation , 2014 .

[14]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[15]  Lennart Ljung,et al.  Experiments with Identification of Continuous Time Models , 2009 .

[16]  Branko D. Kovacevic,et al.  Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors , 2016, EURASIP Journal on Advances in Signal Processing.

[17]  Maria Skyllas-Kazacos,et al.  Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery , 2016 .

[18]  Subhra Paul,et al.  Impact of parameter estimation errors on feedforward current control of permanent magnet synchronous motors , 2016, 2016 IEEE Transportation Electrification Conference and Expo (ITEC).

[19]  Moosa Ayati,et al.  Finite time estimation of actuator faults, states, and aerodynamic load of a realistic wind turbine , 2019, Renewable Energy.

[20]  C YoungPeter.  Refined instrumental variable estimation , 2015 .

[21]  Pierre Kubiak,et al.  Online parameter estimation/tracking for Lithium-ion battery RC model , 2016, 2016 International Renewable and Sustainable Energy Conference (IRSEC).

[22]  Andreas Antoniou,et al.  Robust Recursive Least-Squares Adaptive-Filtering Algorithm for Impulsive-Noise Environments , 2011, IEEE Signal Processing Letters.

[23]  Peter C. Young,et al.  An improved instrumental variable method for industrial robot model identification , 2018 .

[24]  T. Söderström,et al.  Instrumental variable methods for system identification , 1983 .

[25]  Said Doubabi,et al.  SOC estimation of Lithium-ion battery using Kalman filter and Luenberger observer: A comparative study , 2014, 2014 International Renewable and Sustainable Energy Conference (IRSEC).

[26]  Ramazan Havangi.  Joint Parameter and State Estimation Based on Marginal Particle Filter and Particle Swarm Optimization , 2018, Circuits Syst. Signal Process..

[27]  Francesco Borrelli,et al.  A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter , 2015, IEEE Transactions on Intelligent Transportation Systems.

[28]  James Marco,et al.  On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model , 2017 .

[29]  Vinay A. Bavdekar,et al.  Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter , 2011 .

[30]  Xidong Tang,et al.  Li-ion battery parameter estimation for state of charge , 2011, Proceedings of the 2011 American Control Conference.

[31]  Hongye Su,et al.  Nonlinear Observer Design for the State of Charge of Lithium-Ion Batteries , 2014 .

[32]  Marion Gilson,et al.  What has Instrumental Variable method to offer for system identification , 2015 .

[33]  Lin Yang,et al.  Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction , 2015 .

[34]  Hui Li,et al.  State of charge estimation of lithium-ion batteries using fractional order sliding mode observer. , 2017, ISA transactions.

[35]  Peter C. Young,et al.  Refined instrumental variable estimation: Maximum likelihood optimization of a unified Box-Jenkins model , 2015, Autom..

[36]  Nasser L. Azad,et al.  Control‐relevant parameter estimation application to a model‐based PHEV power management system , 2017 .