Abstract
In this paper, an observer-based adaptive fuzzy-neural controller for a class of multi-input multi-output (MIMO) nonlinear systems is developed based on observers used to estimate the time derivatives of the system outputs. The proposed method has the merit that no differentiation of the system output is required in order to avoid the noise amplification associated with numerical differentiation. The stability of the observer-based adaptive fuzzy-neural controller is proven by using strictly-positive-real (SPR) Lyapunov theory. The overall adaptive scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, simulation results are provided to demonstrate the robustness and applicability of the proposed method.
| Original language | English |
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| Pages | 178-183 |
| Number of pages | 6 |
| Publication status | Published - 2000 Dec 1 |
| Event | 26th Annual Conference of the IEEE Electronics Society IECON 2000 - Nagoya, Japan Duration: 2000 Oct 22 → 2000 Oct 28 |
Other
| Other | 26th Annual Conference of the IEEE Electronics Society IECON 2000 |
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| Country | Japan |
| City | Nagoya |
| Period | 2000/10/22 → 2000/10/28 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering