Two-stage gradient-based iterative algorithm for bilinear stochastic systems over the moving data window

2020 ◽  
Vol 357 (15) ◽  
pp. 11021-11041
Author(s):  
Siyu Liu ◽  
Li Xie ◽  
Ling Xu ◽  
Feng Ding ◽  
Ahmed Alsaedi ◽  
...  
Keyword(s):  
Iterative Algorithm ◽  
Stochastic Systems ◽  
Two Stage ◽  
Data Window ◽  
Gradient Based

scholarly journals Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 428 ◽  
Author(s):  
Feng Ding ◽  
Jian Pan ◽  
Ahmed Alsaedi ◽  
Tasawar Hayat
Keyword(s):  
Iterative Algorithm ◽  
System Analysis ◽  
Stochastic Systems ◽  
Controller Design ◽  
Observation Data ◽  
Identification Problems ◽  
Estimation Algorithms ◽  
Gradient Based ◽  
Parameter Identification Problems ◽  
Identification Theory

It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

scholarly journals Iterative Parameter Identification for Fractional-Order Block-Oriented Nonlinear Systems with Application to a Battery Model

2021 ◽  
Author(s):  
Yan Ji ◽  
Junwei Wang ◽  
Xiangxiang Meng
Keyword(s):  
Nonlinear Systems ◽  
Parameter Identification ◽  
Iterative Algorithm ◽  
Fractional Order ◽  
Two Stage ◽  
Hierarchical Identification ◽  
Battery Model ◽  
Block System ◽  
Gradient Based ◽  
Simulation Results

Abstract This paper investigates parameter and order identification of a class of block-oriented nonlinear systems. By using the hierarchical identification principle, the system is divided into two subsystems, which are a linear block system and a nonlinear block system. For the purpose of solving the difficulty of estimating two sets of parameter vectors, the over-parameterization method and the key item separation technique are used, respectively. Therefore, a two-stage over-parameterization gradient-based iterative algorithm and a key term separation two-stage gradient-based iterative algorithm are derived. The simulation results indicate that the proposed algorithms are effective. Finally, the proposed method is evaluated through a battery model. The results show well agreement with the real system outputs.

scholarly journals The optimal convergence factor of the gradient based iterative algorithm for linear matrix equations

Filomat ◽  
2012 ◽  
Vol 26 (3) ◽  
pp. 607-613 ◽  
Author(s):  
Xiang Wang ◽  
Dan Liao
Keyword(s):  
Iterative Algorithm ◽  
Numerical Experiments ◽  
Linear Matrix ◽  
Matrix Equations ◽  
Convergence Factor ◽  
Gradient Based ◽  
Gradient Type ◽  
And Mathematics ◽  
Computers And Mathematics ◽  
Linear Matrix Equations

A hierarchical gradient based iterative algorithm of [L. Xie et al., Computers and Mathematics with Applications 58 (2009) 1441-1448] has been presented for finding the numerical solution for general linear matrix equations, and the convergent factor has been discussed by numerical experiments. However, they pointed out that how to choose a best convergence factor is still a project to be studied. In this paper, we discussed the optimal convergent factor for the gradient based iterative algorithm and obtained the optimal convergent factor. Moreover, the theoretical results of this paper can be extended to other methods of gradient-type based. Results of numerical experiments are consistent with the theoretical findings.

Nonlinear Analysis and Optimization of Diamond Cell Morphing Wings

Aerospace ◽  
2006 ◽  
Author(s):  
Terrence Johnson ◽  
Mary Frecker ◽  
James Joo ◽  
Mostafa Abdalla ◽  
Brian Sanders ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Optimization Procedure ◽  
Two Stage ◽  
Aerodynamic Loads ◽  
Morphing Wings ◽  
Unit Cells ◽  
Gradient Based ◽  
Element Approach ◽  
Multiple Unit ◽  
Elastic Skin

In this work, a design optimization procedure is developed to maximize the energy efficiency of a scissor mechanism for the NextGen's Batwing application. The unit cells are modeled using a finite element approach. The model considers elastic skin, modeled as linear springs, as well as actuator and aerodynamic loads. A nonlinear large displacement analysis is conducted, and the position of the actuator is optimized using Matlab's gradient based optimization algorithm FMINCON. This optimization procedure is used to investigate the effect of different constraints and load cases. The model is expanded to include multiple unit cells and actuators. A two stage optimization process using a Genetic Algorithm and traditional gradient based optimization (FMINCON) is also developed. The two stage optimization is used to optimize actuator position and placement for different constraints and load cases. Results show that placement and position optimization produce small gains in maximizing energy efficiency; morphing using a soft isotropic skin is more efficient than stiff isotropic and anisotropic skin. In addition, the GA did not use the all of the available actuators to maximize energy efficiency.

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