Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Strict-feedback systems present challenges in tracking control due to unknown nonlinearities.
• Existing methods often suffer from complexity explosion and require differentiation.

Purpose of the Study:

• To develop a differentiation-free multiswitching neuroadaptive tracking control for strict-feedback systems.
• To ensure semiglobally/globally ultimately uniformly bounded stability.
• To overcome the 'explosion of complexity' issue.

Main Methods:

• Utilizes adaptive neural network compensators and an auxiliary switched linear controller.
• Employs first-order low-pass filters to manage neural compensator complexity.
• Separates controller-filter pairs for improved stability.
• Incorporates a smooth switching algorithm to address control singularity.

Main Results:

• Achieved bounded stability for filter dynamics, avoiding complexity explosion.
• Demonstrated enhanced flexibility for multiple control objectives.
• Successfully tackled control singularity problems.
• Simulation results validated the proposed control scheme's effectiveness.

Conclusions:

• The proposed differentiation-free neuroadaptive control offers a robust and flexible solution for strict-feedback systems.
• The method effectively manages system complexity and ensures stability.
• This approach advances tracking control for complex nonlinear systems.