Neurodynamics-Based Model Predictive Control of Continuous-Time Under-Actuated Mechatronic Systems
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 9167474 |
| Pages (from-to) | 311-322 |
| Journal / Publication | IEEE/ASME Transactions on Mechatronics |
| Volume | 26 |
| Issue number | 1 |
| Online published | 14 Aug 2020 |
| Publication status | Published - Feb 2021 |
Link(s)
Abstract
This article addresses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems. The control problem is formulated as a global optimization problem based on sampled data, which is solved by using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable. Specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles are elaborated to substantiate the efficacy of the approach.
Research Area(s)
- Model predictive control (MPC), neurodynamic optimization, under-actuated mechatronic systems
Citation Format(s)
Neurodynamics-Based Model Predictive Control of Continuous-Time Under-Actuated Mechatronic Systems. / Wang, Jiasen; Wang, Jun; Han, Qing-Long.
In: IEEE/ASME Transactions on Mechatronics, Vol. 26, No. 1, 9167474, 02.2021, p. 311-322.
In: IEEE/ASME Transactions on Mechatronics, Vol. 26, No. 1, 9167474, 02.2021, p. 311-322.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
