Fixed-Time Prescribed Tracking Control for Stochastic Nonlinear Systems With Unknown Measurement Sensitivity.

Area of Science:

• Control Theory
• Stochastic Systems
• Nonlinear Dynamics

Background:

• Addressing control challenges in uncertain stochastic nonlinear systems is crucial.
• Existing methods often assume known sensor parameters, limiting applicability.
• Input quantization and unknown measurement sensitivity pose significant control hurdles.

Purpose of the Study:

• To develop a fixed-time prescribed tracking control strategy for uncertain stochastic nonlinear systems.
• To account for unknown measurement sensitivity and input quantization.
• To ensure output tracking errors meet predefined performance criteria within a fixed time.

Main Methods:

• Utilizing a novel feedback control algorithm based on unreal measured states.
• Developing a new performance function for fixed-time prescribed performance.
• Employing the backstepping method for adaptive quantized controller design.
• Applying Lyapunov stability theory for rigorous analysis.

Main Results:

• The proposed adaptive quantized controller guarantees fixed-time prescribed performance for output tracking errors.
• All signals in the closed-loop system are proven to be bounded in probability.
• The controller effectively handles unknown measurement sensitivity and input quantization.

Conclusions:

• The developed control algorithm offers a robust solution for fixed-time tracking control of uncertain stochastic nonlinear systems.
• The approach successfully addresses the challenge of unknown sensor sensitivity.
• Simulation results validate the effectiveness and practical applicability of the proposed method.