Parameter estimation for control systems based on impulse responses

The impulse signal is an instant change signal in very short time. It is widely used in signal processing, electronic technique, communication and system identification. This paper considers the parameter estimation problems for dynamical systems by means of the impulse response measurement data. Since the cost function is highly nonlinear, the nonlinear optimization methods are adopted to derive the parameter estimation algorithms to enhance the estimation accuracy. By using the iterative scheme, the Newton iterative algorithm and the gradient iterative algorithm are proposed for estimating the parameters of dynamical systems. Also, a damping factor is introduced to improve the algorithm stability. Finally, using simulation examples, this paper analyzes and compares the merit and weakness of the proposed algorithms.

[1]  Jian Pan,et al.  Image noise smoothing using a modified Kalman filter , 2016, Neurocomputing.

[2]  Wenjian Cai,et al.  Robust decentralized parameter identification for two-input two-output process from closed-loop step responses , 2005 .

[3]  G. M. Malwatkar,et al.  Tuning PID controllers for higher-order oscillatory systems with improved performance. , 2009, ISA transactions.

[4]  Maryam Dehghani,et al.  Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm. , 2014, ISA transactions.

[5]  Feng Ding,et al.  Iterative identification algorithms for input nonlinear output error autoregressive systems , 2016 .

[6]  Dayue Chen,et al.  High-order dynamic modeling and parameter identification of structural discontinuities in Timoshenko beams by using reflection coefficients , 2013 .

[7]  Feng Ding,et al.  Parameter estimation algorithms for multivariable Hammerstein CARMA systems , 2016, Inf. Sci..

[8]  Ling Xu,et al.  Application of the Newton iteration algorithm to the parameter estimation for dynamical systems , 2015, J. Comput. Appl. Math..

[9]  Feng Ding,et al.  Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering , 2017, J. Frankl. Inst..

[10]  Fei Liu,et al.  Equivalent transfer function based multi-loop PI control for high dimensional multivariable systems , 2015 .

[11]  Feng Ding,et al.  The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise , 2017, J. Frankl. Inst..

[12]  Nan Zhao,et al.  Android-based mobile educational platform for speech signal processing , 2017 .

[13]  Sirish L. Shah,et al.  Identification from step responses with transient initial conditions , 2008 .

[14]  Ai-Guo Wu,et al.  Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems , 2014, IET Signal Process..

[15]  F. Ding,et al.  Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems , 2016 .

[16]  Yuhua Chen,et al.  Indirect identification of continuous-time delay systems from step responses , 2011 .

[17]  Feng Ding,et al.  A novel parameter separation based identification algorithm for Hammerstein systems , 2016, Appl. Math. Lett..

[18]  Wendy Van Moer,et al.  Fractional models for modeling complex linear systems under poor frequency resolution measurements , 2013, Digit. Signal Process..

[19]  Qingxia Li,et al.  Array Factor Forming for Image Reconstruction of One-Dimensional Nonuniform Aperture Synthesis Radiometers , 2016, IEEE Geoscience and Remote Sensing Letters.

[20]  Marina H. Murillo,et al.  Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs , 2015, International Journal of Control, Automation and Systems.

[21]  Wei Xing Zheng,et al.  Parameter estimation algorithms for Hammerstein output error systems using Levenberg-Marquardt optimization method with varying interval measurements , 2017, J. Frankl. Inst..

[22]  Giuseppe Fedele.  A new method to estimate a first-order plus time delay model from step response , 2009, J. Frankl. Inst..

[23]  F. Ding,et al.  Performance analysis of the generalised projection identification for time-varying systems , 2016 .

[24]  Feng Ding,et al.  Recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling , 2017, Circuits Syst. Signal Process..

[25]  Feng Ding,et al.  A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay , 2017, Signal Process..

[26]  Egi Hidayat,et al.  Laguerre domain identification of continuous linear time-delay systems from impulse response data , 2012, Autom..

[27]  Ling Xu,et al.  The damping iterative parameter identification method for dynamical systems based on the sine signal measurement , 2016, Signal Process..

[28]  Wei Zhang,et al.  Improved least squares identification algorithm for multivariable Hammerstein systems , 2015, J. Frankl. Inst..

[29]  Alicia Cordero,et al.  A new technique to obtain derivative-free optimal iterative methods for solving nonlinear equations , 2013, J. Comput. Appl. Math..

[30]  Feng Ding,et al.  Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering , 2017, Digit. Signal Process..

[31]  Feng Ding,et al.  Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle , 2017, IET Signal Process..

[32]  Ling Xu,et al.  A proportional differential control method for a time-delay system using the Taylor expansion approximation , 2014, Appl. Math. Comput..

[33]  Hui Zhang,et al.  Synchronization of uncertain chaotic systems via complete-adaptive-impulsive controls , 2017 .

[34]  Sirish L. Shah,et al.  Novel identification method from step response , 2007 .

[35]  Rames C. Panda,et al.  Parameter estimation of integrating and time delay processes using single relay feedback test. , 2011, ISA transactions.

[36]  S. N. Deepa,et al.  Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach , 2011, Swarm Evol. Comput..

[37]  Ling Xu,et al.  Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration , 2015 .

[38]  Qing‐Guo Wang,et al.  Direct identification of continuous time delay systems from step responses , 2001 .

[39]  Hosam E. Emara-Shabaik,et al.  Nonlinear Systems Modeling & Identification Using Higher Order Statistics/Polyspectra , 1996 .

[40]  Su Whan Sung,et al.  Discrete-time frequency response identification method for processes with final cyclic-steady-state , 2014 .

[41]  Jianqiang Pan,et al.  A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems , 2017 .

[42]  Wan Xiangkui,et al.  A T-wave alternans assessment method based on least squares curve fitting technique , 2016 .

[43]  Tao Liu,et al.  A tutorial review on process identification from step or relay feedback test , 2013 .

[44]  F. Ding,et al.  Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems , 2017 .

[45]  T. Hayat,et al.  Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique , 2017 .