Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized...

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.