Asymptotic Tracking Control for a Class of Pure-Feedback Nonlinear Systems

Abstract

This paper studies the adaptive asymptotic tracking problem for a class of unknown nonlinear systems in pure-feedback form. Different from the traditional literatures which only tackle the bounded tracking problem for pure-feedback systems, this paper investigates the asymptotic tracking problem by developing a novel controller design method. Moreover, the differentiable assumption on nonaffine functions is canceled, and only a mild semi-bounded assumption is required as the controllability condition. By utilizing Lyapunov theorem, it is proved that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded, and the output tracking error can converge to zero asymptotically by choosing design parameters appropriately. Finally, a simulation result is presented to verify the effectiveness of the proposed control scheme. INDEX TERMS Asymptotic stability, neural networks, nonlinear control systems, pure-feedback systems.

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…PP algorithm is a more reliable path tracking control algorithm, Figure 3 shows the geometric relationship schematic diagram of PP algorithm, its principle is to control the vehicle turning radius R, so that the vehicle rear axle center point along the arc to reach the reference path target point (gx, gy) with forward-looking distance l, and then based on Ackermann steering model to calculate the required front wheel turning angle δ for control [29]. This control method has simple control and better robustness, even if large lateral errors and curvature changes occur in the tracking process still achieve better tracking results [30].…”