Adaptive NN Control Without Feasibility Conditions for Nonlinear State Constrained Stochastic Systems With Unknown...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Real-world nonlinear stochastic systems often exhibit state time delays and constraints, compromising control quality and stability.
• Existing control methods struggle to simultaneously address both unknown time delays and full state constraints in these systems.

Purpose of the Study:

• To develop an adaptive neural network (NN) controller for nonlinear stochastic systems with full state constraints and unknown time delays.
• To ensure system stability and maintain control quality despite the presence of these challenging system characteristics.

Main Methods:

• Nonlinear mappings transform constrained systems into unconstrained ones, eliminating feasibility condition issues.
• Exponential type Lyapunov-Krasovskii functionals (LKFs) are used to compensate for unknown time-delayed uncertainties.
• Neural networks (NNs) approximate unknown nonlinear functions, while dynamic surface control (DSC) reduces computational load.

Main Results:

• The proposed control method guarantees semiglobal uniform ultimate boundedness for all closed-loop system signals.
• Rigorous proofs confirm the satisfaction of full state constraints throughout the system's operation.
• Simulation examples demonstrate the effectiveness and efficiency of the developed adaptive NN controller.

Conclusions:

• The novel adaptive NN controller successfully addresses the complexities of nonlinear stochastic systems with state constraints and unknown time delays.
• The integration of DSC and LKFs provides a robust and computationally efficient solution.
• The approach offers a significant advancement in controlling challenging real-world systems.