Chengwei Wu

Control and Systems Engineering · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 14 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Chengwei Wu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Shaocheng Tong
  2. Yong-ming Li
  3. Hongyi Li
  4. Shuzhi Sam Ge
  5. C. Wen
  6. Lixian Zhang
  7. Wei He
  8. Dan Wang
  9. Mou Chen
  10. Peng Shi

Who trusts Chengwei Wu overall

  1. Wei He

Papers from Chengwei Wu's perspective

Ordered by how strongly the researchers Chengwei Wu trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (at - Automatisierungstechnik)
  2. Robust Adaptive Control of Uncertain Nonlinear Systems in the Presence of Input Saturation and External Disturbance (IEEE Transactions on Automatic Control)
  3. Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (IEEE Transactions on Neural Networks)
  4. Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones (IEEE transactions on fuzzy systems)
  5. Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint (IEEE Transactions on Systems, Man, and Cybernetics: Systems)
  6. Adaptive output control of nonlinear systems with uncertain dead-zone nonlinearity (IEEE Transactions on Automatic Control)
  7. Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems (IEEE transactions on fuzzy systems)
  8. Robust adaptive control of a class of nonlinear systems with unknown dead-zone (Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228))
  9. Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay (IEEE transactions on fuzzy systems)
  10. Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form (at - Automatisierungstechnik)

Their papers