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A Novel Error-Compensation Control for a Class of High-Order Nonlinear Systems With Input Delay
IEEE Transactions on Neural Networks and Learning Systems
|October 14, 2017
Summary
This study introduces a novel adaptive neural control for nonlinear systems, improving tracking precision and stability by compensating for errors and input delays. The new method enhances control performance in complex systems.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence
Background:
- Existing control methods for high-order nonlinear systems struggle with output lag, peak errors, and initial tracking error instability.
- Inequality amplification in high-order systems and assumptions on unknown coefficients limit control precision.
Purpose of the Study:
- To propose a novel tracking error-compensation-based adaptive neural control scheme.
- To address limitations in existing control methods for nonlinear systems with unknown nonlinearities and input delay.
Main Methods:
- Developed three error-compensation terms: prediction/compensation, auxiliary signal, and damping.
- Utilized radial basis function neural networks to approximate complex system dynamics online.
- Implemented a robust compensation signal to handle input delays using past control values.
Main Results:
- Successfully compensated for tracking errors, reducing lag and peak errors.
- Achieved improved control precision without requiring exact knowledge of unknown control coefficient lower bounds.
- Demonstrated semiglobally uniformly ultimately boundedness of all closed-loop signals.
Conclusions:
- The proposed adaptive neural control scheme effectively enhances tracking performance and stability in high-order nonlinear systems.
- The method overcomes limitations of prior approaches, offering robust control even with unknown nonlinearities and input delays.