Neural-Network-Based Adaptive Constrained Control for Switched Systems Under State-Dependent Switching Law.

Area of Science:

• Control Theory
• Nonlinear Systems
• Adaptive Control

Background:

• Switched uncertain nonlinear systems pose challenges due to unknown dynamics and state constraints.
• Existing control methods may struggle to simultaneously address both uncertainty and state limitations.

Purpose of the Study:

• To develop an adaptive tracking control strategy for switched uncertain nonlinear systems with state constraints.
• To ensure boundedness of system signals, accurate output tracking, and adherence to state constraints.

Main Methods:

• Utilized the multiple Lyapunov function approach combined with radial basis function neural networks (RBFNNs) for function approximation.
• Employed barrier Lyapunov functions (BLFs) to enforce state constraints.
• Designed a state-dependent switching law and applied the backstepping technique to construct the adaptive neural network (NN) controller.

Main Results:

• All signals in the closed-loop system were proven to be bounded.
• The system output achieved tracking of the reference signal within a compact set.
• State constraints were successfully maintained throughout the operation under the proposed control strategy.

Conclusions:

• The developed adaptive NN controller effectively manages switched uncertain nonlinear systems with state constraints.
• The combination of RBFNNs, BLFs, and a state-dependent switching law provides a robust control solution.
• Simulation results validate the proposed method's effectiveness in achieving stable tracking and constraint satisfaction.