4.8 Article

Finite-Time Adaptive Fuzzy Tracking Control for a Class of Nonlinear Systems With Full-State Constraints

期刊

IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 29, 期 8, 页码 2246-2255

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2996387

关键词

Nonlinear systems; Backstepping; Adaptive systems; Convergence; Fuzzy logic; Fuzzy control; Control systems; Adaptive fuzzy control; backstepping; finite-time convergence; full-state constrains; nonlinear system

资金

  1. National Natural Science Foundation of China [61603204, 61973179]
  2. Shandong Province Outstanding Youth Fund [ZR2018JL020]
  3. Science and Technology Support Plan for Youth Innovation of Universities in Shandong Province [2019KJN033]
  4. Taishan Scholar Special Project Fund [TSQN20161026]

向作者/读者索取更多资源

This article presents a new command filtered backstepping based finite-time adaptive fuzzy tracking control scheme for unknown nonlinear systems with full-state constraints. The effectiveness of the control method is demonstrated through simulations.
In this article, a new command filtered backstepping based finite-time adaptive fuzzy tracking control scheme for a class of unknown nonlinear systems with full-state constraints is established. First, the proposed finite-time command filter will filtering the virtual control signal and get the intermediate control signal within finite-time, so the problem of calculating complexity will not occur in the backstepping process. Then, the fraction-power-based error compensate signal is set up, which can eliminate the influence of filtering error on the control performance. Considering that the unknown nonlinearities exist in the system, the fuzzy logic system based adaptive control technique is used to deal with them, and only one parameter needs to be estimated. It is shown that the states will not violate the prescribed constrains, all the signals in the closed-loop system are bounded in finite-time and the tracking error can converge to the desired neighborhood of the origin in finite time under the barrier Lyapunov function and fraction-power-based virtual control signals. Finally, the effectiveness of the control method is shown by the simulations.

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