Guaranteeing Prescribed Performance Control for Gyrostabilized Platform with Unknown Control Direction Preceded by Hysteresis

Abstract

This paper investigates the problem of precise and quick tracking for gyrostabilized platform (GSP) with unknown hysteresis, unknown control directions, and unknown compound disturbance. Firstly, the dynamic model of GSP is transformed into a strict feedback formulation by designed FD to facilitate the backstepping control system. Secondly, performance functions are constructed at each step of backstepping design to force tracking errors to fall within the prescribed boundaries. Besides, through ingenious transformation, radial basis function neural network (RBFNN) is applied to estimate the unknown control gains preceded by hysteresis. Hence, the problem of prescribed performance control with unknown compound disturbances, unknown hysteresis, and unknown control directions is creatively solved. Furthermore, the exploited controllers are accurate model independent, which guarantees satisfactory robustness of control laws against unknown uncertainties. Finally, the stability of the closed-loop control system is confirmed via Lyapunov stability theory, and numerical simulations are given for a GSP to validate the effectiveness of the proposed controller.

Finite‐Time Observer‐Based Adaptive Control of Switched System with Unknown Backlash‐Like Hysteresis

“…To capture hysteresis dynamics and achieve precise control, many scholars have made efforts to eliminate its effect. e hysteresis models can be roughly divided into two classes in [16,17]: the operator-based hysteresis model [17][18][19] and the differential equation-based hysteresis model, such as Bouc-Wen model [20,21] and backlash-like model [22][23][24][25][26]. For instance, in [18], Mayergoyz described a new approach to the scalar Preisach model of hysteresis which emphasized its phenomenological nature and mathematical generality.…”