Zhong-ke Shi

Control and Systems Engineering · 2008–2014

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Zhong-ke Shi trusts overall

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

  1. Mou Chen
  2. Shuzhi Sam Ge
  3. T. Zhang
  4. Min Wang
  5. Jay A. Farrell
  6. S. Darbha
  7. B. Xu
  8. Yahui Li

Who trusts Zhong-ke Shi overall

  1. Mou Chen
  2. Wei He
  3. Zhouhua Peng

Papers from Zhong-ke Shi's perspective

Ordered by how strongly the researchers Zhong-ke Shi 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 dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form (at - Automatisierungstechnik)
  3. Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (IEEE Transactions on Neural Networks)
  4. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (at - Automatisierungstechnik)
  5. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks (IEEE Transactions on Neural Networks)
  6. Command filtered backstepping (American Control Conference)
  7. Adaptive NN Backstepping Output-Feedback Control for Stochastic Nonlinear Strict-Feedback Systems With Time-Varying Delays (IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics))
  8. Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique (IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics))
  9. Dynamic Surface Control of Constrained Hypersonic Flight Models with Parameter Estimation and Actuator Compensation
  10. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form (IEEE Transactions on Neural Networks)

Their papers