Abstract
The problem of non-affine time-varying nonlinear control systems is addressed in this work through an adaptive state-space neuro-fuzzy control scheme. It combines a eight-layered neuro-fuzzy model to approximate non-affine nonlinear systems' dynamics with a state feedback quadratic stabilising controller. Both the neuro-fuzzy model and controller are updated online within a constrained unscented Kalman filter framework. The proposed generalised state-space neuro-fuzzy model is shown to be an universal approximator, and stability conditions derived for time-varying closed loop systems. Results from a benchmark MIMO system demonstrate the effectiveness of the proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 1602-1615 |
| Journal | IEEE Transactions on Fuzzy Systems |
| Volume | 27 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2019 |
Keywords
- Kalman filter
- Neuro-Fuzzy Control
- Nonlinear time-varying systems
- recursive learning
- unscented transform