Command Filter-Based Adaptive Neural Control Design for Nonstrict-Feedback Nonlinear Systems With Multiple Actuator...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems often exhibit complex behaviors and face challenges from actuator constraints.
• Existing control methods may struggle with actuator nonlinearities and computational complexity.
• Adaptive control and neural networks offer potential solutions for enhanced system management.

Purpose of the Study:

• To develop an adaptive neural-network control scheme for nonlinear systems with multiple actuator constraints.
• To address actuator nonlinearity using an equivalent transformation method.
• To mitigate the complexity explosion issue inherent in advanced control designs.

Main Methods:

• An equivalent transformation method was employed to handle actuator nonlinearities.
• A command filter technique was utilized to manage computational complexity.
• An adaptive neural-network tracking backstepping control strategy was designed, integrating backstepping algorithms and command filtering.
• Neural network approximation was leveraged for adaptive control capabilities.

Main Results:

• The proposed control scheme guarantees the boundedness of all system variables.
• The output tracking error was shown to remain within a small, bounded region near the origin.
• Simulation results validated the effectiveness and availability of the developed control strategy.

Conclusions:

• The adaptive neural-network command-filtered tracking control scheme effectively manages nonlinear systems with actuator constraints.
• The integration of command filtering and neural networks provides a robust solution for complex control problems.
• The proposed method offers a promising approach for achieving precise tracking performance in constrained systems.