Discrete-time super-twisting controller using neural networks
| Autor: | Miguel Hernandez-Gonzalez, Esteban A. Hernandez-Vargas |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer simulation Computer science Cognitive Neuroscience 02 engineering and technology Plot (graphics) Computer Science Applications Nonlinear system 020901 industrial engineering & automation Discrete time and continuous time Artificial Intelligence Control theory 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Block (data storage) |
| Zdroj: | Neurocomputing. 447:235-243 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2021.03.060 |
| Popis: | This paper presents a neural super-twisting controller for a class of discrete-time nonlinear systems. A general plot of the control problem consists in considering a discrete-time nonlinear system with a block controllable structure, and assuming that the equations of the nonlinear system have virtual controls, a discrete-time super-twisting algorithm is applied such that each equation follows a desired trajectory until the real control is achieved. On the other hand, taking into account the capacity of neural networks to learn the behaviour of complex systems, it is proposed a neural network with a block controllable structure for identifying and controlling a discrete-time nonlinear system. Neural network weights are trained with the cubature Kalman filter algorithm. To show the effectiveness of the proposed neural super-twisting controller, a numerical simulation is performed on a Quanser 2-DOF helicopter. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect