Feedback linearization control for uncertain nonlinear systems via generative adversarial networks.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Uncertain nonlinear systems pose significant control challenges.
• Existing feedback linearization controllers (FLCs) struggle with system uncertainties.
• Generative Adversarial Networks (GANs) offer potential for learning complex system dynamics.

Purpose of the Study:

• To develop a novel approach for learning a feedback linearization controller (FLC) for uncertain nonlinear systems using GANs.
• To enhance the reference tracking performance of input-output uncertain nonlinear systems.
• To provide theoretical guarantees for the convergence and stability of the learned controller.

Main Methods:

• Utilizing generative adversarial networks (GANs) to estimate system uncertainty.
• Employing a minimax two-player optimization framework for controller learning.
• Ensuring generator structure strict convexity and enhanced adversarial loss to mitigate GANs mode collapse.
• Validating the approach through comprehensive simulations and practical experiments.

Main Results:

• The proposed GAN-based FLC significantly improves reference tracking performance in uncertain nonlinear systems.
• Theoretical guarantees for convergence and stability of the robust FLC are established.
• The method effectively addresses mode collapse challenges common in GAN training.
• Experimental results confirm the superiority and efficacy of the proposed approach.

Conclusions:

• Generative adversarial networks provide a powerful framework for learning robust feedback linearization controllers for uncertain nonlinear systems.
• The developed approach offers enhanced performance and stability guarantees, overcoming limitations of traditional methods.
• This work demonstrates a promising direction for applying advanced AI techniques in advanced control system design.