Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy
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View abstract on PubMed
Summary
This summary is machine-generated.This study presents a novel optimal fixed-time control strategy for large-scale nonlinear systems. The method ensures fast, bounded tracking performance using neural networks and reinforcement learning.
Area Of Science
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence
Background
- Large-scale nonlinear systems present significant control challenges due to their complexity and interconnectedness.
- Achieving optimal tracking performance with guaranteed convergence times is crucial for many applications.
- Existing methods often struggle with convergence speed and parameter tuning in complex systems.
Purpose Of The Study
- To develop an optimal fixed-time tracking control strategy for nonstrict-feedback large-scale nonlinear systems.
- To ensure tracking errors converge within prescribed performance bounds in a fixed time.
- To enhance the convergence rate and simplify the control algorithm using neural networks.
Main Methods
- Utilizing fixed-time control techniques combined with a simplified reinforcement learning algorithm.
- Developing novel critic and actor neural network updating laws for optimal control.
- Implementing the minimum parameter method to reduce adaptive law complexity.
- Incorporating prescribed performance control to bound tracking errors.
Main Results
- The proposed control strategy guarantees tracking errors converge within prescribed bounds in a fixed time.
- The method accelerates convergence rates and simplifies the optimal control algorithm.
- All closed-loop signals are demonstrated to be bounded within a fixed time interval.
- Simulation results validate the effectiveness of the proposed control strategy.
Conclusions
- The developed optimal fixed-time control strategy effectively addresses tracking control for large-scale nonlinear systems.
- The integration of neural networks, reinforcement learning, and prescribed performance offers a robust solution.
- The approach provides faster convergence and guaranteed performance bounds, applicable to complex dynamic systems.
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