Tie-shan Li

Control and Systems Engineering · 2008–2021

A researcher in the Science corpus: 10 papers; trusts 36 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

Whom Tie-shan Li trusts overall

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

  1. Shuzhi Sam Ge
  2. Dan Wang
  3. Shaocheng Tong
  4. T. Zhang
  5. Cong Wang
  6. Reza Olfati-Saber
  7. Wei Ren
  8. Jie Huang
  9. Wei Lin
  10. Yong-ming Li

Who trusts Tie-shan Li overall

  1. Yong-ming Li
  2. Shaocheng Tong
  3. Zhouhua Peng
  4. Hongyi Li
  5. Mou Chen
  6. Yong‐Hua Liu
  7. Jun Wang
  8. Kangkang Sun
  9. Qi Zhou
  10. Jinpeng Yu

Papers from Tie-shan Li's perspective

Ordered by how strongly the researchers Tie-shan Li trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Dynamic surface control for a class of nonlinear systems (IEEE Transactions on Automatic Control)
  2. Adaptive NN control of uncertain nonlinear pure-feedback systems (at - Automatisierungstechnik)
  3. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form (IEEE Transactions on Neural Networks)
  4. Information flow and cooperative control of vehicle formations
  5. Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form (at - Automatisierungstechnik)
  6. Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance (IEEE Transactions on Automatic Control)
  7. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (at - Automatisierungstechnik)
  8. A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems (IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans)
  9. Flocking for multi-agent dynamic systems: algorithms and theory
  10. Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems (at - Automatisierungstechnik)

Their papers