Adaptive robust finite-time control of mobile robot systems with unmeasurable angular velocity via bioinspired neurodynamics approach

Abstract

This paper addresses the adaptive robust finite-time bioinspired neurodynamics control (ARFBNC) for a class of mobile robots with unmeasurable angular velocity and multiple time-varying bounded disturbances. The error system of the mobile robot is decomposed into two subsystems based on the system model. The state feedback control laws with disturbance feed-forward compensators are designed for the two subsystems, respectively. The ARFBNC method is designed based on the state feedback control laws and two subsystems. The stability conditions in the form of linear matrix inequalities (LMIs) are derived by introducing the Lyapunov–Krasovskii functional. Unlike other works, both the unmeasurable angular velocity and multiple time-varying bounded disturbances are estimated effectively. The smooth bounded outputs are obtained and the sharp jumps of initial values for the state errors are reduced. By introducing the Lyapunov–Krasovskii functional, the closed-loop system is asymptotically stable and the state errors converge to an adjustable bounded region. Finally, three examples are given to show the effectiveness and advantage of the proposed methods.