scholarly journals Research and Analysis of Generalized Predictive Identification Algorithm Based On Nonlinear System in Control Field of Hydropower Industry

2021 ◽  
Vol 1802 (3) ◽  
pp. 032128
Author(s):  
Xiaoping Gou ◽  
Wanjun Zhang ◽  
Feng Zhang ◽  
Jingxuan Zhang ◽  
Jingyi Zhang ◽  
...  
Keyword(s):  
Nonlinear System ◽  
Identification Algorithm ◽  
Control Field ◽  
Hydropower Industry

scholarly journals Research and analysis of constrained generalized predictive identification algorithm based on the control field of hydropower industry

2021 ◽  
Vol 651 (2) ◽  
pp. 022078
Author(s):  
Xiaoping Gou ◽  
Wan Jun Zhang ◽  
Feng Zhang ◽  
Jingxuan Zhang ◽  
Jingyi Zhang ◽  
...  
Keyword(s):  
Identification Algorithm ◽  
Control Field ◽  
Hydropower Industry

Iterative learning identification method for parameters of macro traffic flow model

2020 ◽  
pp. 1-9
Author(s):  
Yuan Chen
Keyword(s):  
Nonlinear System ◽  
Parameter Identification ◽  
Traffic Flow ◽  
Discrete Time ◽  
Flow Model ◽  
Iterative Learning ◽  
System Model ◽  
Identification Algorithm ◽  
Theoretical Derivation ◽  
Traffic Flow Model

The macro traffic flow model of expressway is transformed into a general discrete-time nonlinear system model including this model by using the repeatability of the macro traffic flow model, and then a parameter identification algorithm based on iterative learning is designed for this general discrete-time nonlinear system model. The convergence of this parameter identification scheme is proved by strict theoretical derivation and robustness. Simulation results verify the effectiveness of the algorithm.

scholarly journals Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Huiyi Hu ◽  
Xiao Yongsong ◽  
Rui Ding
Keyword(s):  
Nonlinear System ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
System Model ◽  
Noise Model ◽  
Identification Algorithm ◽  
Separation Principle ◽  
Model Decomposition ◽  
Stochastic Gradient Algorithm ◽  
Input Nonlinear System

An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.

scholarly journals Recursive identification algorithm for dynamic systems with output backlash and its convergence

2009 ◽  
Vol 19 (4) ◽  
pp. 631-638 ◽  
Author(s):  
Ruili Dong ◽  
Qingyuan Tan ◽  
Yonghong Tan
Keyword(s):  
Nonlinear System ◽  
Dynamic Systems ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Wiener Model ◽  
Nonlinear Part ◽  
Numerical Example ◽  
Wiener System ◽  
Proposed Model ◽  
Linearized Model

Recursive identification algorithm for dynamic systems with output backlash and its convergenceThis paper proposes a recursive identification method for systems with output backlash that can be described by a pseudo-Wiener model. In this method, a novel description of the nonlinear part of the system, i.e., backlash, is developed. In this case, the nonlinear system is decomposed into a piecewise linearized model. Then, a modified recursive general identification algorithm (MRGIA) is employed to estimate the parameters of the proposed model. Furthermore, the convergence of the MRGIA for the pseudo-Wiener system with backlash is analysed. Finally, a numerical example is presented.

Investigating Dynamics of One Weakly Nonlinear System with Delay Argument

2018 ◽  
Vol 50 (1) ◽  
pp. 20-38 ◽  
Author(s):  
Denis Ya. Khusainov ◽  
Jozef Diblik ◽  
Jaromir Bashtinec ◽  
Andrey V. Shatyrko
Keyword(s):  
Nonlinear System ◽  
Weakly Nonlinear ◽  
Delay Argument ◽  
System With Delay ◽  
Weakly Nonlinear System

Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

2020 ◽  
Vol 48 (4) ◽  
pp. 287-314
Author(s):  
Yan Wang ◽  
Zhe Liu ◽  
Michael Kaliske ◽  
Yintao Wei
Keyword(s):  
Elastic Deformation ◽  
Estimation Algorithm ◽  
Force Sensor ◽  
Euler Method ◽  
Rolling Contact ◽  
Body Motion ◽  
Identification Algorithm ◽  
Active Perception ◽  
Kinematics Model ◽  
Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

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