Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Existing control methods struggle with uncertain, nonaffine multi-input-multi-output (MIMO) nonlinear systems.
• Pure-feedback subsystems with varying orders present significant control challenges.
• Nonaffine control variables complicate the design of stable tracking controllers.

Purpose of the Study:

• To develop a novel adaptive neural tracking control strategy for uncertain MIMO nonlinear systems.
• To address the challenges posed by nonaffine dynamics and subsystem couplings.
• To guarantee semiglobal uniform ultimate boundedness of all closed-loop signals.

Main Methods:

• Employing the mean value theorem to transform nonaffine systems into a strict-feedback form.
• Designing a systematic, singularity-free adaptive neural control procedure.
• Utilizing neural networks to approximate unknown system nonlinearities.

Main Results:

• Successfully removed couplings among subsystems, avoiding circular control construction.
• Guaranteed semiglobal uniform ultimate boundedness for all closed-loop signals.
• Ensured system outputs converge to a small neighborhood of desired trajectories.

Conclusions:

• The proposed adaptive neural tracking control strategy is effective for uncertain MIMO nonlinear systems.
• The method provides robust performance and stability guarantees.
• Simulation studies validate the theoretical framework and practical applicability.