Optimal PD-type networked iterative learning algorithm based fault estimation for repetitive systems with delays, packet losses, sensor saturation and sensor failure

2021 ◽  
Author(s):  
Samba Aimé Hervé ◽  
Yeremou Tamtsia Aurelien ◽  
Nneme Nneme Leandre
Keyword(s):  
Learning Algorithm ◽  
Iterative Learning ◽  
Fault Estimation ◽  
Sensor Failure ◽  
Packet Losses ◽  
Sensor Saturation ◽  
Repetitive Systems

scholarly journals Networked Iterative Learning Fault Diagnosis Algorithm for Systems with Sensor Random Packet Losses, Time-varying Delays, Limited Communication and Actuator Failure : Application to the Hydroturbine Governor System

2021 ◽  
Vol 16 ◽  
pp. 244-252
Author(s):  
Samba Aime Herve ◽  
Yeremou Tamtsia Aurelien ◽  
Hermine Som Idellette Judith ◽  
Nneme Nneme Leandre
Keyword(s):  
Fault Diagnosis ◽  
Design Method ◽  
Lyapunov Stability Theory ◽  
Iterative Learning ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Time Varying ◽  
Actuator Failure ◽  
Packet Losses ◽  
Limited Communication

An iterative learning fault diagnosis (ILFD) algorithm for networked control systems (NCSs) subject to random packet losses, time-varying delays, limited communication and actuator failure is proposed in this paper. Firstly, in order to evaluate the effect of fault on system between every iteration, the information of state error and information of fault tracking estimator from the preceding iteration are used to improve the fault estimation achievement in the actual iteration. The state variable, the Bernoulli process of random packet losses, network communication delay, limited communication and actuator failure are introduced to establish an extended statespace model of the system. Secondly combining Lyapunov stability theory for linear repetitive processes and linear matrix inequality (LMI) technique, new sufficient condition for the existence of an iterative learning fault diagnosis is established. Finally, the feasibility and effectiveness of the proposed design method is illustrated on a dynamic hydroturbine governing system model based on Matlab/Simulink and TrueTime toolbox

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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