System Transformation-Based Neural Control for Full-State-Constrained Pure-Feedback Systems via Disturbance Observer

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Pure-feedback nonlinear systems often present challenges due to full-state constraints.
• Designing controllers for these systems is complicated by nonaffine input signals and unknown control gains.
• Existing methods may require knowledge of control gain signs and bounds, limiting applicability.

Purpose of the Study:

• To propose a novel disturbance observer-based adaptive neural control (ANC) scheme for full-state-constrained pure-feedback nonlinear systems.
• To develop a method that transforms constrained states into unconstrained ones, simplifying controller design.
• To improve disturbance rejection, robustness, and reduce computational complexity.

Main Methods:

• A nonlinear transformation function is used to handle state constraints within a unified framework.
• An auxiliary first-order filter creates an augmented nonlinear system, addressing nonaffine input challenges.
• A nonlinear disturbance observer (NDO) is integrated to improve disturbance rejection capabilities.
• Second-order filters and backstepping are combined with the NDO-based ANC for controller synthesis.

Main Results:

• The proposed scheme successfully confines all system states within predefined bounds.
• It eliminates the need for prior knowledge of control gain signs and bounds.
• Enhanced robustness and improved disturbance rejection capabilities of the closed-loop system are demonstrated.
• A reduction in computational burden compared to existing methods is achieved.

Conclusions:

• The novel disturbance observer-based adaptive neural control scheme is effective for full-state-constrained pure-feedback nonlinear systems.
• The system transformation and NDO integration provide a robust and computationally efficient control solution.
• Simulation results validate the scheme's performance in maintaining state constraints and rejecting disturbances.