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In this paper, an adaptive robust stabilization problem is dealt with for a class of uncertain strict‐feedback nonlinear systems in the presence of unknown structure uncertainties, external disturbances, and unknown time‐varying virtual control coefficients. It is not required to know the upper bounds of external disturbances, as well as the upper and lower bounds of unknown time‐varying virtual control coefficients. The controller is designed by adopting backstepping. In addition, to avoid suffering from the problem of ‘explosion of terms’, a dynamic surface control approach is employed by introducing the first‐order low‐pass filter. Furthermore, at every step of the backstepping design procedure, fuzzy logic systems are used to approximate the unknown structure uncertainties. In particular, the norms of weight matrices and the upper bounds of approximation errors of fuzzy logic systems are supposed to be unknown. It is also shown that the proposed controller can guarantee the uniform boundedness of uncertain strict‐feedback nonlinear systems. Finally, the simulation for a single‐link manipulator actuated by a brush DC motor is carried out to illustrate the validity of the proposed controller.

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In this article, an adaptive dynamic surface control approach is proposed for uncertain strict-feedback systems (SFSs) to guarantee both the prescribed transient tracking performance and the asymptotic tracking while realizing the accurate parameter estimation. It is assumed that SFSs are subject to linearly parametric uncertainties in both the drift terms and the control coefficients. A new inequality on the arctangent function, which can be widely used in the robust or the adaptive control designs, is established. Owing to this inequality, nonlinear robust filters with arctangent functions are designed and embedded into the backstepping control algorithm to avoid the "differential explosion" problem. Moreover, an improved forgetting-factor-based parameter estimation error reconstruction mechanism is proposed. And the obtained parameter estimation errors are integrated into the adaptive laws to achieve the accurate parameter estimation. Furthermore, the projection operator is applied to avoid the singularity problem of the control law. Besides, an asymmetric error transformation is introduced to restrict the tracking error within the prescribed performance envelope. It is proved that the tracking error and the parameter estimation errors asymptotically converge to zero and the tracking error satisfies the prescribed transient performance. Finally, the effectiveness of the proposed control approach is validated in terms of the single-link manipulator actuated by a brush DC motor.