Neuroadaptive Output-Feedback Tracking Control for Stochastic Nonlower Triangular Nonlinear Systems With Dead-Zone...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Stochastic Systems Analysis

Background:

• Stochastic nonlinear systems with nonlower triangular structures present significant control challenges.
• Dead-zone inputs and unmeasured states further complicate the design of effective tracking controllers.
• Existing control methods often struggle to guarantee stability and performance under these conditions.

Purpose of the Study:

• To develop a neuroadaptive tracking control framework for stochastic nonlower triangular nonlinear systems with dead-zone inputs and unmeasured states.
• To extend stability criteria for these complex systems.
• To ensure all system signals remain bounded.

Main Methods:

• A state observer is designed to estimate unmeasured states, creating an error dynamics system.
• A neural network-based tracking controller is developed using dynamic surface control and variable separation techniques.
• Backstepping design framework is employed for controller synthesis.

Main Results:

• The proposed framework successfully addresses unmeasured states and dead-zone inputs.
• Stability analysis confirms that all system signals remain bounded.
• Simulation examples validate the effectiveness and practicality of the neuroadaptive control strategy.

Conclusions:

• The developed neuroadaptive tracking control framework is effective for stochastic nonlower triangular nonlinear systems with dead-zone inputs and unmeasured states.
• The integration of dynamic surface control and state observers provides a robust solution.
• The approach offers a promising direction for advanced control applications in complex dynamic systems.