Prescribed Performance Tracking Control Under Uncertain Initial Conditions: A Neuroadaptive Output Feedback Approach

Area of Science:

• Control Systems Engineering
• Nonlinear System Dynamics
• Artificial Intelligence in Control

Background:

• Prescribed performance control (PPC) methods often struggle with initial condition dependence and convergence time predictability.
• Nonlinear systems with nontriangular structures and partial state measurements pose significant control challenges.

Purpose of the Study:

• To develop an output-based finite-time tracking control strategy for nonlinear nontriangular systems.
• To address uncertainties in initial conditions and partially measurable states.
• To overcome algebraic loop issues inherent in nontriangular structures.

Main Methods:

• Integration of neural networks with variable separation techniques to construct a simplified state observer.
• Application of an error transformation to achieve prescribed performance.
• Development of a control scheme circumventing algebraic loops.

Main Results:

• The proposed method ensures finite-time tracking control with prescribed accuracy and convergence rate.
• The strategy effectively eliminates dependence on initial conditions.
• Algebraic loop issues in nontriangular systems are successfully circumvented.

Conclusions:

• The novel control strategy provides robust and predictable tracking performance for challenging nonlinear systems.
• The method offers a significant advancement over conventional prescribed performance control techniques.