Abstract
The adaptive practical prescribed-time (PPT) neural control is studied for multi-input multi-output (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. Firstly, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator.
| Original language | English |
|---|---|
| Number of pages | 11 |
| Journal | IEEE Transactions on Artificial Intelligence |
| DOIs | |
| Publication status | Published - 2024 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Backstepping
- Convergence
- dynamic surface control
- Maximum likelihood detection
- MIMO communication
- nonlinear filter
- Nonlinear filters
- nonlinear system
- Prescribed-time neural control
- robot manipulator
- Stability analysis
- Uncertainty