Low-Cost Approximation-Based Adaptive Tracking Control of Output-Constrained Nonlinear Systems.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Adaptive Control Theory

Background:

• Addressing tracking control for pure-feedback nonlinear systems with asymmetric output constraints presents significant challenges.
• Existing methods often require restrictive conditions on boundaries or complex control structures for constrained systems.

Purpose of the Study:

• To develop a low-cost neuroadaptive tracking control solution for pure-feedback nonlinear systems under asymmetric output constraints.
• To eliminate restrictive conditions on constraining boundaries and handle both constrained and unconstrained cases uniformly.

Main Methods:

• Construction of a novel output-dependent universal barrier function (ODUBF) to uniformly handle constrained and unconstrained scenarios.
• Development of a single parameter estimator to reduce computational burden from neural network (NN)-based approximators, making the design computationally inexpensive.
• Analysis of closed-loop system signals for semiglobal uniform ultimate boundedness.

Main Results:

• The proposed neuroadaptive control prevents violation of asymmetric output constraints.
• Tracking errors converge to an adjustable neighborhood of the origin.
• All signals within the closed-loop system are demonstrated to be semiglobally uniformly ultimately bounded.

Conclusions:

• The novel ODUBF and single parameter estimator provide an effective and computationally efficient solution for neuroadaptive tracking control.
• The method successfully addresses asymmetric output constraints in pure-feedback nonlinear systems.
• Numerical simulations validate the effectiveness of the proposed control strategy.