scholarly journals Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Li Feng ◽  
Ke Zhang ◽  
Yi Chai ◽  
Shuiqing Xu ◽  
Zhimin Yang
Keyword(s):  
Nonlinear System ◽  
Iterative Learning ◽  
System Model ◽  
Fault Estimation ◽  
System State ◽  
Numerical Examples ◽  
Trial Length ◽  
Average Factor ◽  
Learning Law ◽  
Random Trial

An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under λ-norm constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.

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 On a nonlinear system of Riemann-Liouville fractional differential equations with semi-coupled integro-multipoint boundary conditions

2021 ◽  
Vol 19 (1) ◽  
pp. 760-772
Author(s):  
Ahmed Alsaedi ◽  
Bashir Ahmad ◽  
Badrah Alghamdi ◽  
Sotiris K. Ntouyas
Keyword(s):  
Nonlinear System ◽  
Differential Equations ◽  
Boundary Conditions ◽  
Fractional Differential Equations ◽  
Fixed Point Theory ◽  
Point Theory ◽  
Numerical Examples ◽  
Multipoint Boundary ◽  
Multipoint Boundary Conditions ◽  
Fractional Differential

Abstract We study a nonlinear system of Riemann-Liouville fractional differential equations equipped with nonseparated semi-coupled integro-multipoint boundary conditions. We make use of the tools of the fixed-point theory to obtain the desired results, which are well-supported with numerical examples.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

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