Novel L1 neural network adaptive control architecture with guaranteed transient performance

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Adaptive control systems are crucial for managing uncertain nonlinear systems.
• Ensuring transient performance alongside stable tracking remains a challenge.
• Neural network-based controllers offer potential but require robust theoretical guarantees.

Purpose of the Study:

• To propose a novel neural network adaptive control architecture.
• To guarantee both stable tracking and transient performance for uncertain nonlinear systems.
• To provide a framework for verification and validation of neural network adaptive controllers.

Main Methods:

• Development of a new neural network adaptive control architecture.
• Incorporation of a low-pass filter in the feedback loop.
• Analysis of the L1 gain of a cascaded system involving the filter and reference model.

Main Results:

• The proposed architecture ensures that both input and output signals follow a desired linear system during the transient phase.
• Transient performance is enforced by increasing the adaptation gain, facilitated by the low-pass filter.
• Theoretical conditions for guaranteed transient performance are established based on system L1 gain and Lipschitz constants.

Conclusions:

• The novel architecture successfully achieves guaranteed transient performance for uncertain nonlinear systems.
• The developed theoretical framework supports the verification and validation of neural network adaptive controllers.
• Simulation results validate the effectiveness of the proposed control strategy.