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Beibei Ren
Researcher at Texas Tech University
Publications - 103
Citations - 5150
Beibei Ren is an academic researcher from Texas Tech University. The author has contributed to research in topics: Nonlinear system & Adaptive control. The author has an hindex of 28, co-authored 100 publications receiving 3990 citations. Previous affiliations of Beibei Ren include University of California, San Diego & National University of Singapore.
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Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
TL;DR: The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design, and the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis.
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Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
TL;DR: A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: for any initial compact set, how to determine a priori the compact superset on which NN approximation is valid; and how to ensure that the arguments of the unknown functions remain within the specified compact supersets.
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Brief paper: Control of nonlinear systems with time-varying output constraints
TL;DR: It is shown that asymptotic output tracking is achieved without violation of the time-varying constraint, and that all closed loop signals remain bounded.
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Sliding mode control to stabilization of cascaded heat PDE-ODE systems subject to boundary control matched disturbance
TL;DR: The existence and uniqueness of the solution for the closed-loop system are proved, the monotonicity of the "reaching condition" is presented without differentiation of the sliding mode function, and the numerical simulations validate the effectiveness of this method for the system with periodic and normal random disturbances respectively.
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Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input
TL;DR: In this article, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input.