Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems
IEEE Transactions on Neural Networks and Learning Systems
|January 24, 2017
Summary
This study introduces a novel adaptive control method using neural networks for uncertain nonlinear discrete-time systems. The approach ensures system stability and achieves good tracking performance, validated by simulations.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Nonlinear Dynamics
Background:
- Existing control methods for similar systems primarily focus on continuous-time domains.
- Nonlinear discrete-time systems with non-strict feedback structures present significant control design and stability analysis challenges.
Purpose of the Study:
- To develop an adaptive control approach using neural approximation for uncertain nonlinear discrete-time systems.
- To address the complexities of multi-input multi-output systems with non-strict feedback structures.
Main Methods:
- A new recursive procedure is developed for controller design and stability analysis.
- Semirecurrent neural approximation is employed to mitigate noncausal problems in discrete-time non-strict feedback systems.
- Lyapunov difference approach is utilized for stability proofs.
Main Results:
- The proposed adaptive control method ensures semiglobal, ultimately uniform boundedness of all closed-loop system signals.
- Effective tracking performance is guaranteed for the considered class of systems.
- The controller's feasibility is demonstrated through a simulation example.
Conclusions:
- The developed neural approximation-based adaptive control is effective for uncertain nonlinear discrete-time systems.
- The method successfully handles non-strict feedback structures and discrete-time complexities.
- This approach offers a viable solution for stabilizing and controlling such complex systems.
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