Command Filter-Based Adaptive NN Control for MIMO Nonlinear Systems With Full-State Constraints and Actuator...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Strict-feedback nonlinear systems present control challenges due to state constraints and actuator hysteresis.
• Approximating unknown nonlinear functions and managing complexity are critical in adaptive control design.

Purpose of the Study:

• To develop an adaptive neural network (NN) control strategy for multi-input, multi-output (MIMO) nonlinear systems.
• To address challenges posed by full-state constraints and actuator hysteresis in strict-feedback systems.

Main Methods:

• Utilizing Radial Basis Function Neural Networks (RBFNNs) for approximating unknown nonlinear functions.
• Employing a command filter to mitigate the 'explosion of complexity' issue.
• Applying a one-to-one nonlinear mapping to transform the constrained system into a pure-feedback system.

Main Results:

• A novel NN control method is proposed for the transformed pure-feedback system.
• The stability of the closed-loop system is rigorously proven using Lyapunov stability theory.
• Tracking errors are demonstrated to converge to small residual sets.

Conclusions:

• The proposed adaptive NN control method effectively handles strict-feedback MIMO nonlinear systems with full-state constraints and actuator hysteresis.
• The method ensures system stability and achieves satisfactory tracking performance.
• Simulation results validate the efficacy and robustness of the presented control approach.