Dynamic learning from adaptive neural control for full-state constrained strict-feedback nonlinear systems.

Area of Science:

• Control Theory
• Machine Learning
• Nonlinear Systems

Background:

• Strict-feedback systems with full-state constraints present significant learning and control challenges.
• Existing control methods often struggle to maintain system states within predefined bounds while enabling adaptive learning.

Purpose of the Study:

• To develop a novel adaptive neural control scheme for strict-feedback systems with full-state constraints.
• To ensure all closed-loop signals remain uniformly bounded and system states stay within constraint ranges.
• To achieve enhanced control performance through learning and adaptation.

Main Methods:

• System transformation to convert constrained systems into quasi-pure-feedback unconstrained systems.
• Utilizing a single neural network (NN) for identifying unknown dynamics in the transformed system.
• Employing dynamic surface control design for stability analysis and controller synthesis.
• Transforming NN weight convergence into an exponential stability problem for linear time-varying systems.

Main Results:

• A novel adaptive neural control scheme is developed ensuring uniform boundedness of closed-loop signals.
• All system states are guaranteed to remain within the predefined constraint range.
• Precise convergence of NN weights is achieved, enabling the creation of a learning controller.
• Simulations demonstrate the viability and effectiveness of the proposed control strategy.

Conclusions:

• The proposed adaptive neural control scheme effectively addresses learning and control issues in strict-feedback systems with full-state constraints.
• The method ensures system stability and constraint satisfaction while leveraging neural network-based learning.
• The stored NN weights facilitate a learning controller for improved performance under constraints.