Adaptive Predefined Time Control for Stochastic Switched Nonlinear Systems With Full-State Error Constraints and...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Stochastic Systems Analysis

Background:

• Existing control methods for stochastic nonlinear systems often lack guaranteed convergence times.
• Full-state error constraints and chattering are significant challenges in adaptive quantized control.
• Arbitrary switching in systems complicates stability analysis and control design.

Purpose of the Study:

• To develop a neural network adaptive quantized control strategy for switching stochastic nonlinear systems.
• To ensure system stabilization within a predefined time frame under arbitrary switching.
• To address full-state error constraints and mitigate the chattering phenomenon.

Main Methods:

• Introduction and establishment of predefined-time stability criteria for stochastic nonlinear systems.
• Utilizing a hysteresis quantizer to decompose nonlinear functions, thereby avoiding chattering.
• Employing a universal barrier Lyapunov function to manage full-state error constraints.
• Demonstrating system stability using the common Lyapunov function approach.

Main Results:

• The proposed control method achieves probabilistic practical predefined-time stabilization (PPTS) for all closed-loop signals.
• The system output demonstrates accurate tracking of the specified reference signal.
• Simulated examples confirm the efficacy of the developed control technique.

Conclusions:

• The study successfully presents a novel control approach for complex switching stochastic nonlinear systems.
• The method guarantees predefined-time stability and effective error constraint handling.
• The findings offer a robust solution for applications requiring precise and timely system control.