Contents I. Introduction
The stabilization of practical plants in the presence of uncertainties is a crucial issue that has garnered extensive attention in the research field of control. Out of various control methods available for nonlinear systems (NSs), including sliding mode control (SMC) [1], robust control [2], and adaptive control [3], backstepping has emerged as a significant tool for handling control tasks in NSs [4], [5]. To tackle the challenge of unknown functions in system models, neural networks (NNs) and other approaches that can approximate functions are employed. These techniques are well-suited for handling such challenges [7], [8]. As a result of their properties, numerous noteworthy outcomes have been produced, as reported in [9] and [10], and the references therein. To name a few, the results in [11] addressed an adaptive control problem for nonsmooth NSs using a control strategy that combines NNs and the backstepping method. In [12], with the assistance of NNs, a neural FTC scheme is investigated for switching NSs. A neural adaptive control method was put forward for a family of switched NSs utilizing the backstepping in [13]. To alleviate the inherent computational complexity associated with backstepping, the dynamic surface control (DSC) approach was introduced to improve the issue mentioned-above [14]. The DSC method was then extended to address the control problem for pure-feedback NSs [15], in which a novel control scheme utilizing NN estimation was proposed. The above-mentioned results are primarily considered for constraint-free NSs.
