Linearization and state estimation of unknown discrete-time nonlinear dynamic systems using recurrent neurofuzzy
1Dept. of Electron. & Comput. Sci., Southampton Univ.
Summary
This study introduces a novel recurrent neurofuzzy network for approximate feedback linearization of unknown nonlinear dynamic systems. This method enhances state estimation for complex systems, offering practical benefits like model parsimony and learning convergence.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Nonlinear Dynamics
Background:
- Model-based control is established for linear systems but challenging for unknown nonlinear systems.
- Data-driven model identification and linearization are crucial for practical nonlinear system control.
- Existing feedback linearization methods face theoretical and practical limitations.
Purpose of the Study:
- To review linearization techniques using neural networks and their associated difficulties.
- To propose a novel recurrent neurofuzzy network for approximate feedback linearization.
- To investigate the application of this method for state estimation in nonlinear systems.
Main Methods:
- A recurrent neurofuzzy network with analysis of variance (ANOVA) decomposition structure was developed.
- A learning algorithm was designed for the proposed neurofuzzy network.
- The method was applied to state estimation problems for unknown discrete-time nonlinear dynamic systems.
Main Results:
- The proposed method enables approximate feedback linearization for a broader class of nonlinear systems.
- Simulations demonstrated practical properties including model parametric parsimony and learning convergence.
- The neurofuzzy network proved effective for state estimation in complex, unknown nonlinear systems.
Conclusions:
- The recurrent neurofuzzy network offers a viable approach for approximate feedback linearization.
- This technique expands the applicability of neural networks in nonlinear system control and state estimation.
- The method shows promise for real-world applications involving complex unknown nonlinear dynamics.
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