Fault detection for iterative learning control of time-varying systems

An iterative learning control and fault detection technology, which is applied in manufacturing tools, measuring devices, material defect testing, etc., and can solve problems such as poor performance conservative learning algorithms and unstable ILC algorithms.

CN114867570AActive Publication Date: 2022-08-05NUCOR CORP
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2022-08-05

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Abstract

The double-roller casting system comprises a pair of casting rollers rotating in opposite directions, wherein an adjustable roller gap is formed between the casting rollers; a casting roll controller configured to adjust a roll gap between the casting rolls in response to a control signal; a cast strip sensor that measures a parameter of the cast strip and generates a strip measurement signal; and an iterative learning control (ILC) controller that receives the tape measurement signal and provides a control signal to the cast roll controller. The ILC controller includes a fault detection algorithm that receives the control signal and the tape measurement signal and generates a fault detection signal indicating when a fault condition is detected, and an iterative learning control algorithm that generates the control signal. The fault detection algorithm indicates a fault condition when it is detected that the control signal exceeds an upper control saturation threshold or the ILC controller operates in an instable state.
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Description

Background technique

[0001] Iterative Learning Control (ILC) is a control method that is particularly suitable for processes that are repetitive in nature. When applying ILC in practice, the algorithm should be set up to ensure that 1) the system will remain stable, and 2) the performance specifications will be met. However, these goals may conflict with each other, especially when the plant model contains uncertainty and / or iteratively changing dynamics.

[0002] Recent studies have examined the convergence and robustness properties of ILC algorithms applied to iteratively varying or non-repetitive systems. More specifically, previous research developed an optimal design procedure for ILCs that balances system performance with robust stability criteria. However, a limitation of this approach is that satisfying the robust stability criterion requires knowledge of the upper bound on the uncertainty of the input and output operators. In some applications, the upper bound may ...

Examples

Embodiment Construction

[0025] refer to figure 1 , the ILC controller 100 controls the device 102 according to an aspect of the present invention. Device 102 may include a potentially non-repetitive system. The ILC controller 100 includes an ILC algorithm 106 and a fault detection algorithm 104 . The fault detection algorithm 104 detects when the ILC algorithm 106 applied to the device 102 either 1) violates assumptions for guaranteeing asymptotic stability or 2) fails to meet predefined performance specifications or both. If operating conduction or stability requirements are not met, the fault detection algorithm 104 generates a fault detection signal indicative of a fault condition.

[0026] Assuming U and Y are Banach spaces with suitable norm, use || · || to denote the vector and induced operator norm in the appropriate space. In some embodiments, the class of ILC systems includes linear time-invariant systems of the form:

[0027]

[0028] where y k ∈ Y is the output signal, u k ∈U is t...