Abstract
In this paper, a genetic adaptive fuzzy-neural control scheme is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates backstepping design into the genetic algorithm with a backstepping-based fitness function. Using the backstepping-based fitness function, the genetic algorithm can be used to adjust the parameters of the fuzzy-neural networks in order to instantaneously generate the appropriate control strategy. The genetic algorithm has a simplified procedure with the backstepping-based fitness function which is used to evaluate the real-time stability of the closed-loop systems. To illustrate the feasibility and applicability of the proposed method, simulation and experimental results are provided.
| Original language | English |
|---|---|
| Pages (from-to) | 207-219 |
| Number of pages | 13 |
| Journal | International Journal of Computational Intelligence in Control |
| Volume | 12 |
| Issue number | 2 |
| Publication status | Published - 2020 Jul 1 |
Keywords
- Adaptive fuzzy-neural control
- Genetic algorithm
- Nonlinear systems
ASJC Scopus subject areas
- Biotechnology
- Computational Mechanics
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence