Barrier Lyapunov Function Based Finite-Time Backstepping Control of Quadrotor with Full State Constraints

2018 ◽  
Author(s):  
Zhongjun Hu ◽  
Qiang Chen ◽  
Yi Hu ◽  
Cong Chen
Keyword(s):  
Lyapunov Function ◽  
Finite Time ◽  
State Constraints ◽  
Backstepping Control ◽  
Barrier Lyapunov Function ◽  
Full State ◽  
Full State Constraints

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

2021 ◽  
pp. 014233122198912
Author(s):  
Yan Wei ◽  
Pingfang Zhou ◽  
Yueying Wang ◽  
Dengping Duan ◽  
Jiwei Tang
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
State Constraints ◽  
Multiple Input Multiple Output ◽  
Backstepping Control ◽  
Attitude Tracking ◽  
Multiple Input ◽  
Full State ◽  
Input Multiple Output ◽  
Full State Constraints

This paper investigates the issue of finite-time tracking control for multiple-input–multiple-output nonlinear systems subject to uncertainties and full state constraints. To deal with full state constraints directly, integral barrier Lyapunov functionals (iBLF) are introduced. By using finite-time stability theory, an iBLF-based adaptive finite-time neural control scheme is presented. To solve the problem of “explosion of complexity” in the design of traditional backstepping control, a new finite-time convergent differentiator is presented. Through stability analysis, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded, the finite time convergence can be guaranteed, and the state constraints are never violated. Finally, the attitude tracking simulations for an autonomous airship are conducted to verify the effectiveness of the proposed scheme.

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