Adaptive backstepping control design for MEMS gyroscope based on function approximation techniques with input saturation and output constraints. (January 2022)

• Record Type: Journal Article
• Authors: Zirkohi, Majid Moradi
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: MEMS gyroscope -- Barrier Lyapunov Function (BLF) -- Hermite polynomials -- Input saturation -- Lyapunov stability theory -- Backstepping control approach; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107547
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Addressing an adaptive backstepping control design for the MEMS gyroscope. Proposing a simple structure, and preventing the ''explosion of terms" problem. Investigating the robustness of the proposed method. Applicability of the proposed robust control scheme to different nonlinear systems. Conducting the stability of the whole control system. Abstract: An efficient adaptive backstepping control method is presented in the current study for controlling the micro-electro-mechanical system (MEMS) triaxial gyroscope. In this approach, practical considerations, including output constraints and input saturation, are taken into account. A Barrier Lyapunov Function (BLF) is used so that non-transgression of the constraints is ensured. In the suggested control method, for approximating the lumped uncertainties and addressing the problem of explosion of complexity, Hermite polynomials are used as a simple but effective function approximation approach as a universal approximator. Therefore, the resulting control approach is much simpler with minimal learning parameters. The Lyapunov stability theory is used so that the semiglobally uniformly ultimately boundedness of the closed-loop signals with proper tracking error convergence is guaranteed. According to the results of the comparative simulation, the superiority of this approach is confirmed.