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This article concerns the output feedback tracking control problem for a class of single-input-single-output (SISO) nonlinear systems in a semistrict feedback form, which possesses both matched and mismatched uncertainties, including parametric uncertainties and unknown nonlinear disturbances. An active disturbance rejection adaptive output feedback control scheme is proposed via backstepping method, in which parametric uncertainties are handled by adaptation law and disturbances are estimated by a series of linear extended state observers (LESOs). The learning burden of LESOs is much alleviated due to active compensation of parametric uncertainties via adaptation. In addition, both matched and mismatched disturbances are estimated in the construction of LESO for each channel of the considered nonlinear plant. Hence, all various of uncertainties can be mostly compensated. Consequently, prescribed transient performance and steady-state tracking accuracy can be guaranteed. Theoretical analysis reveals the tracking error can be made arbitrarily small. Furthermore, exponentially asymptotic output tracking can be achieved when unknown disturbances are constants. Finally, comparative simulation studies are presented to illustrate the effectiveness of the proposed scheme.

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This article concerns with the tracking control problem for a class of control-affine nonlinear systems subject to input saturation, parametric uncertainties and unmodeled uncertainties. The proposed scheme consists of a nested-saturation-function-based controller integrated with feedforward model compensation. A saturated linear extended state observer (SLESO) and parameter adaptation law are constructed to compensate for the unmodeled uncertainties and parametric uncertainties, respectively. By using these mechanisms, the steady-state tracking performance can be guaranteed through uncertainty compensation. Additionally, the issue of conservativeness in the input saturation problem is effectively addressed through online-updating the available unsaturated region, which improves transient performance. The proposed scheme results in asymptotic stability when unmodeled uncertainties are constants and ultimate boundedness when unmodeled uncertainties are time-varying.Finally, simulation studies are presented to demonstrate the effectiveness of the proposed approach.