Adaptive neural optimized control for uncertain strict-feedback systems with unknown control directions and pre-set performance

Abstract

In this paper, we develop an adaptive neural strategy based on optimized backstepping (OB) technology for strict-feedback systems with unknown control directions and pre-set performance. First, the OB technique is used to construct each optimized virtual controller and optimized actual controller in backstepping, so as to achieve global optimization. Second, the problem of unknown control directions is overcome by using a Nussbaum-type function. Next, a time-varying switching function and the quartic barrier Lyapunov function are introduced to solve the control problem of the pre-set time and accuracy. The proposed control strategy makes the tracking error of the system converge to the pre-set accuracy within the pre-set time. At the same time, all of the signals of the closed-loop system are bounded. Finally, two set simulations verify the effectiveness of the proposed control strategy.