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

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

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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.

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