Finite time adaptive learning for tracking control of constraint nonlinear systems via command filtered output feedback

Abstract

AbstractThis article is concerned with the adaptive learning finite‐time output feedback control design for a class of high‐order nonlinear systems involving full state constraints. First, a state observer is designed to estimate the unmeasurable states and the radial basis function neural networks are used to identify unknown functions in the system. Then based on the state observer, we develop an adaptive learning finite‐time output feedback controller by combining the command filtered technique and error‐tracking method. In addition, the “explosion of complexity” problem can be avoided and the filtered errors caused by dynamic surface control can be compensated by the newly proposed command filter control. By constructing a novel log‐type barrier Lyapunov function, it can be guaranteed that the full state constraints are not violated and all the variables in the controlled systems are bounded in finite time. Finally, two simulation examples are carried out to demonstrate the effectiveness of the proposed algorithm.