Contents I. Introduction
Over the past few decades, the problems related to the field of nonlinear control have attracted great attention [1]–[5]. Many approaches for controller design have been investigated, such as backstepping control, dynamic surface control, adaptive control, and so on. Among them, the adaptive backstepping control method not only solves the tracking control problem of nonlinear systems with mismatched conditions but also ensures the stability of the closed-loop system. For example, Liang et al. [6] proposed an attitude stabilization controller for spacecraft with control input saturation and measurement uncertainties. Qi et al. [7] investigated the sliding mode control (SMC) for a class of stochastic switched systems with the semi-Markov process by utilizing an adaptive event-triggered mechanism. Although the adaptive backstepping control method has some advantages, it is not feasible to use this method alone for the controlled system with unknown nonlinear functions. Therefore, based on the approximation property of the neural networks or fuzzy logic systems, a variety of research works on intelligent adaptive control for nonlinear systems have been achieved [8]–[15]. For instance, for a class of networked nonlinear systems with time-varying communication delay, an adaptive fuzzy predictive controller is designed and analyzed in [16]. The authors considered a class of single-input and single-output (SISO) uncertain nonstrict feedback nonlinear systems and come up with a new control method by using the property of fuzzy basis functions in [17]. However, the above design schemes do not consider the control problem of output or state constraints.
