Command filter based adaptive control of asymmetric output-constrained switched stochastic nonlinear systems

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Stochastic Processes

Background:

• Switched stochastic nonlinear systems present significant control challenges.
• Asymmetric output constraints complicate controller design and stability analysis.

Purpose of the Study:

• To develop an adaptive tracking control strategy for switched stochastic nonlinear systems with asymmetric output constraints.
• To address the issue of unknown nonlinear functions and stochastic disturbances.

Main Methods:

• A nonlinear mapping (NM) transforms the constrained system into an unconstrained one.
• Command filtering and neural networks (NNs) are employed to manage complexity and unknown dynamics.
• Stochastic Lyapunov function method is used for stability analysis.

Main Results:

• The developed adaptive neural controller guarantees semi-globally uniformly ultimately boundedness (SGUUB) of all signals.
• The controller ensures the output constraint is satisfied.
• Desired signal tracking is achieved within a small domain of the origin.

Conclusions:

• The proposed adaptive control scheme effectively handles asymmetric output constraints in switched stochastic nonlinear systems.
• Simulation results validate the controller's performance and feasibility.