Asymptotic tracking control of strict‐feedback non‐linear systems with output constraints in the presence of input saturation

In this study, the asymptotic tracking control problem is addressed for known and unknown non‐linear systems in the strict‐feedback form with time‐varying output constraints, input saturation, and external disturbances. A barrier Lyapunov function is employed to prevent transgression of the output constraints. Neural networks are applied to approximate the unknown functions. To deal with the input saturation effects and/or neural networks reconstruction errors, the Nussbaum gain technique is suggested. The proposed approach guarantees the boundedness of all the closed‐loop signals, and for the first time, the asymptotic tracking property is achieved for the strict‐feedback non‐linear systems, while the actual output remains in the output constraints, despite input saturation and external disturbances. Two simulation examples have validated the effectiveness of the proposed results.