Fuzzy adaptive output feedback control for uncertain nonlinear systems with unknown control gain functions and unmodeled dynamics

Adaptive Neural Tracking Control for Nonstrict-Feedback Nonlinear Systems with Unknown Control Gains via Dynamic Surface Control Method

“…This method was proposed by Nussbaum [27], and can effectively solve the control problem of a system with unknown control gain. In [28][29][30], adaptive control laws were addressed by applying the Nussbaum gain function method for nonlinear systems with unknown control directions. In [31], the authors proposed an observer-based adaptive fuzzy outputfeedback control law for uncertain nonlinear systems with input quantization and unknown control direction in which the control law design combines the backstepping technique and Nussbaum function.…”

“…The radial basis function neural network (RBFNN) and Nussbaum gain function control technique are introduced to design the final control law. The main contributions of this paper are as follows: (1) a class of nonstrict-feedback nonlinear systems with unknown control gains is considered, and differently from [25,26], the unknown control gain exists in each subsystem; (2) compared with [28,29], in this paper, the Nussbaum gain function is considered in each step of recursive design, and thus the design of the virtual control law can be realized by using the dynamic surface control technique; and (3) the proposed control law can guarantee that all signals in the closed-loop system are bounded and the tracking error can converge to an arbitrarily small domain of zero.…”

Adaptive fuzzy tracking control for non‐strict‐feedback non‐linear systems with non‐constant delays and full state constraints

“…To deal with the non-linearity and uncertainty in the control of non-strict-feedback systems, in recent years, adaptive fuzzy backstepping control has become a reliable control strategy [5,6]. In [7], Liu proposed an adaptive fuzzy control (AFC) strategy based on the finite-time backstepping method.…”

Robust adaptive control of uncertain nonlinear systems with unmodeled dynamics using command filter

“…In recent years, the advanced control design methods, such as robust adaptive control, 1 fuzzy logic control, 2 guaranteed cost control, 3 backstepping control, 4 optimal control, 5 and consensus control, 6,7 have been studied for various purposes and requirements of nonlinear dynamic systems and applied to many fields, including power systems, 8,9 robotic systems, 10,11 circuit systems, 12 multiagent systems, 13,14 and wastewater treatment 15 . The robust adaptive backstepping can simplify controller design due to its flexibility, especially for large‐scale complex systems.…”

“…This might make the prescribed performance difficult to be realized. Hence, unmodeled dynamics receives increasing attention, and numerous effective control techniques emerge in References 12,30,31. This article constructs a dynamic signal based on the input‐to‐state stability theory to deal with the unmodeled dynamics, where the radial basis function neural networks (RBFNNs) are used to approximate the unmodeled dynamics related uncertain functions.…”