scholarly journals Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels

2021 ◽  
Author(s):  
Lixun Huang ◽  
Lijun Sun ◽  
Tao Wang ◽  
Weihua Liu ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Control Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Optimal Input ◽  
Random Delays ◽  
Data Dropouts

scholarly journals Optimal input filters for iterative learning control systems with additive noises, random delays and data dropouts in both channels

2021 ◽  
Author(s):  
Lixun Huang ◽  
Lijun Sun ◽  
Tao Wang ◽  
Weihua Liu ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Control Systems ◽  
Data Transmission ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Random Delays ◽  
Error Covariance ◽  
State Augmentation ◽  
Convergence Performance ◽  
Data Dropouts

In wireless networked iterative learning control systems, the controller is separated from the plant, and additive noises, random delays and data dropouts arise in both sensor-to-controller and controller-to-actuator channels. In order to guarantee the convergence performance of such systems with the effect of these uncertainties, an input filter is designed based on a proportional iterative learning controller, so that updated inputs can be filtered at the actuator side. Specifically, two data transmission processes are first developed to describe the mix of those uncertainties in both channels by Bernoulli and Gaussian distributed variables with known distributions. Based on state augmentation, the two data transmission processes are further combined with the iterative learning process of controllers to build a unified filtering model. According to this unified model, an optimal filter is designed via the projection theory and implemented at the actuator side to filter the updated inputs in iteration domain. Moreover, the convergence performance of the filtering error covariance matrix is proved theoretically. Finally, some numerical results are given to illustrate the effectiveness of the proposed method.

Eliminating Structural Error in Real-Time Adjustable Four-Bar Path Generators Using Iterative Learning Control

2004 ◽  
Author(s):  
Hong-Jen Chen ◽  
Richard W. Longman ◽  
Meng-Sang Chew
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Path Generation ◽  
Structural Error ◽  
Four Bar Linkage

Fundamental concepts of Iterative Learning Control (ILC) are applied to path generating problems in mechanisms. As an illustration to such class of problems, an adjustable four-bar linkage is used. The coupler point of a four-bar traces a coupler curve that will in general deviate from the desired coupler path. Except at the precision points, the coupler curve will exhibit some structural error, which is the deviation from the specified curve. The structural error will repeat itself every cycle at exactly the same points over the range of interest. Since ILC is a methodology that was developed to handle similar repetitive errors in control systems, it is believed that it will be well served to apply it to this class of problems. Results show that ILC can be simple to implement, and it is found to be very well suited for such path generation problems.

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