Adaptive robust finite-time control of mobile robot systems with unmeasurable angular velocity via bioinspired neurodynamics approach. (June 2019)

• Record Type: Journal Article
• Authors: Zheng, Wei; Wang, Hongbin; Zhang, Zhiming; Wang, Hongrui
• Is Part Of: Engineering applications of artificial intelligence. Volume 82(2019)
• Page Start: 330
• Page End: 344
• Publication Date: 2019-06
• Subjects: Unmeasurable angular velocity -- Bounded disturbances -- Subsystems -- Adaptive robust finite-time control -- Bioinspired neurodynamics method -- Lyapunov–Krasovskii functional; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.04.009
• Languages: English
• ISSNs: 0952-1976

Abstract

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.