4.8 Article

Fuzzy Adaptive Event-Triggered Control for a Class of Uncertain Nonaffine Nonlinear Systems With Full State Constraints

Journal

IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 29, Issue 4, Pages 904-916

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2966185

Keywords

Nonlinear systems; Adaptive systems; Backstepping; Artificial neural networks; Fuzzy logic; Fault tolerance; Fault tolerant systems; Adaptive control; event-triggered control (ETC); full state constraints; fuzzy approximation; nonaffine nonlinear system

Funding

  1. National Natural Science Foundation of China [61673098, 61773221, 61773013, U173110085]
  2. Natural Science Foundation of Liaoning Province of China [20180551190]
  3. Scientific Research Foundation of Liaoning Educational Committee of China [2017LNZD05]
  4. National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1A2B2004671]
  5. National Research Foundation of Korea [4220200113789] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This article presents a fuzzy adaptive tracking control method for a class of uncertain nonaffine nonlinear systems, and demonstrates the effectiveness of the control strategy and algorithm proposed.
This article is concerned with the fuzzy adaptive tracking control problem for a class of uncertain nonaffine nonlinear systems with full state constraints and event-triggered inputs. First, based on the mean-value theorem and the input compensation method, the control input signal of nonaffine nonlinear term is successfully decoupled, and the remained compound nonlinear function can be approximated by fuzzy logic systems. Then, an improved event-triggered control strategy including the fixed threshold, the event-trigger control input, and the decreasing function of tracking errors, is given to update the actual control input. Correspondingly, the computation burden of the communication procedure is effectively alleviated. In addition, the developed control scheme guarantees that all the states of the closed-loop systems are not to violate the predefined compact sets by constructing barrier Lyapunov functions. Also, the system output tracking error can converge to a desired neighborhood of the origin. Finally, the validity of the proposed approach is demonstrated by the simulation results.

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