Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Nonsmooth nonlinear systems present significant control challenges due to discontinuous properties.
• Classical control methods often struggle with systems exhibiting input deadzone and saturation.

Purpose of the Study:

• To develop an output-feedback adaptive neural network (NN) control strategy for nonsmooth nonlinear systems.
• To address challenges posed by input deadzone and saturation in these systems.
• To ensure semiglobally uniformly ultimately bounded (SGUUB) stability for the closed-loop system.

Main Methods:

• Utilizing the mean-value theorem to transform nonsmooth input nonlinearities into smooth affine forms with bounded errors.
• Applying approximation theorems and Filippov's differential inclusion theory to convert the nonsmooth system into an equivalent smooth model.
• Designing an observer and employing adaptive backstepping techniques with a logarithmic barrier Lyapunov function (BLF).

Main Results:

• A novel output-feedback adaptive NN control strategy was successfully developed.
• The proposed method effectively handles input deadzone and saturation in nonsmooth systems.
• A new stability criterion guarantees SGUUB of all closed-loop system signals.
• Simulations on Chua's oscillator validated the control algorithm's effectiveness.

Conclusions:

• The proposed adaptive NN control strategy offers an effective solution for stabilizing nonsmooth nonlinear systems with input nonlinearities.
• The method ensures robust performance and signal boundedness, crucial for practical applications.
• The study contributes a new stability criterion and a validated control framework for complex nonlinear systems.