Enhanced Control of Nonlinear Systems Under Control Input Constraints and Faults: A Neural Network-Based Integral Fuzzy Sliding Mode Approach
Guangyi Yang, Stelios Bekiros, Qijia Yao, Jun Mou, Ayman A Aly, Osama R Sayed, Kevin H Knuth · 2024
Read Enhanced Control of Nonlinear Systems Under Control Input Constraints and Faults: A Neural Network-Based Integral Fuzzy Sliding Mode Approach by Guangyi Yang, Stelios Bekiros, Qijia Yao, Jun Mou, Ayman A Aly, Osama R Sayed, Kevin H Knuth (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11675582.txt.
Description
Guangyi Yang and co-authors design a controller for nonlinear systems whose actuators can saturate, stutter, or fail outright. Most control theory quietly assumes the hardware obeys. Real motors hit limits and develop faults, and controllers that ignore this lose the plant. Their 2024 design fuses three tools into one scheme.
A neural network estimates what the model cannot say, fuzzy logic tunes the controller's behavior on the fly, and an integral sliding mode backbone holds the system on its target surface. An intelligent observer watches the actuator itself, estimating faults and lost authority so the controller can compensate. The blend also damps the chattering that sliding modes are notorious for. Control engineers get a scheme built for the world where parts break.
Catalog record
| CREATOR | Guangyi Yang, Stelios Bekiros, Qijia Yao, Jun Mou, Ayman A Aly, Osama R Sayed, Kevin H Knuth |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |