Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control
Summary
This study introduces an adaptive neural network (NN) control for stochastic nonlinear systems with time-varying delays and unknown control directions. The method ensures system stability and reduces online learning time.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Stochastic Systems
Background:
- Stochastic nonlinear systems with time-varying delays present significant control challenges.
- Unknown control directions complicate controller design and stability analysis.
Purpose of the Study:
- To develop an adaptive output-feedback neural network (NN) control scheme.
- To address stochastic nonlinear time-varying delay systems with unknown control directions.
Main Methods:
- A linear state transformation groups unknown control coefficients.
- Nussbaum functions and backstepping techniques handle unknown control directions.
- A single NN compensates for unknown nonlinear terms using delayed output.
Main Results:
- The proposed control scheme ensures all closed-loop system signals are bounded in probability.
- Parameter estimation is simplified by estimating maximums, reducing online learning time.
- Simulation results validate the effectiveness of the adaptive control strategy.
Conclusions:
- The developed adaptive NN control is effective for the targeted complex systems.
- The methodology offers a practical approach to controlling systems with unknown dynamics and delays.
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