Adaptive Neural Control of Constrained MIMO Nonlinear Systems With Asymmetric Input Saturation and Dead Zone

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Complex nonlinear systems often exhibit challenging characteristics like asymmetric input saturation and dead zones.
• Full state-function constraints are common in real-world applications, complicating control design.
• Existing adaptive control methods may struggle with these combined nonlinearities and constraints.

Purpose of the Study:

• To develop a robust adaptive neural control strategy for multiple-input-multiple-output (MIMO) nonlinear systems.
• To effectively handle asymmetric input saturation, dead zones, and full state-function constraints.
• To ensure boundedness of all signals and convergence of tracking error within a defined region.

Main Methods:

• Utilizing radial basis function (RBF) neural networks (NNs) for approximating unknown nonlinear functions.
• Employing a Nussbaum function to manage unknown control gains.
• Applying a time-varying barrier Lyapunov function (BLF) to address state-function constraints.
• Integrating the backstepping design methodology for a systematic control scheme.

Main Results:

• The proposed control scheme successfully manages asymmetric input saturation and dead zones.
• State-function constraints are strictly enforced, preventing violation throughout closed-loop operation.
• All signals within the closed-loop system are proven to be bounded.
• The tracking error converges to a small neighborhood around the origin, demonstrating effective control.

Conclusions:

• The presented adaptive neural control scheme offers a robust solution for MIMO nonlinear systems with significant nonlinearities and constraints.
• The integration of RBF NNs, Nussbaum functions, and BLFs provides a powerful framework for advanced control design.
• The method's effectiveness is validated through simulations and application to a mass-spring-damper system.