Dynamic Event-Based Adaptive Fixed-Time Control for Uncertain Strict-Feedback Nonlinear Systems With State Constraints

Ganghui Shen, Panfeng Huang, Zhiqiang Ma, Fan Zhang, Yuanqing Xia

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Actuators
  • Artificial neural networks
  • Backstepping
  • Convergence
  • Dynamic event-triggered mechanism (DETM)
  • Fans
  • Nonlinear systems
  • Trajectory
  • fixed-time control
  • neural networks (NNs)
  • state constraints
  • strict-feedback nonlinear systems

Fingerprint

Dive into the research topics of 'Dynamic Event-Based Adaptive Fixed-Time Control for Uncertain Strict-Feedback Nonlinear Systems With State Constraints'. Together they form a unique fingerprint.

Cite this