Command-filter-based adaptive neural tracking control for a class of nonlinear MIMO state-constrained systems with...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence in Control

Background:

• State-constrained nonlinear systems present significant control challenges due to inherent complexities.
• Input delay and saturation in multi-input and multi-output (MIMO) systems further complicate control design.
• Adaptive control is crucial for handling uncertainties in dynamic systems.

Purpose of the Study:

• To develop an adaptive neural tracking control scheme for nonlinear MIMO state-constrained systems.
• To address the challenges posed by input delay and saturation.
• To improve system tracking performance while managing computational load.

Main Methods:

• Utilized neural networks for approximating unknown nonlinear uncertainties.
• Introduced barrier Lyapunov functions to ensure state constraint satisfaction.
• Employed a smooth non-affine approximate function and an auxiliary system to handle input saturation with time delay.
• Combined command filtering backstepping with a filtering error compensating system.

Main Results:

• Successfully developed an adaptive neural tracking control strategy.
• Effectively managed state constraints, input delay, and saturation.
• Demonstrated avoidance of differentiation explosion and reduced computational burden.
• Achieved significant improvement in system tracking performance.

Conclusions:

• The proposed adaptive neural tracking control strategy is feasible and effective.
• The integration of neural networks and advanced control techniques addresses complex system dynamics.
• The approach offers a robust solution for nonlinear MIMO systems with challenging constraints.