Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

By Shuzhi Sam Ge, Jing Wang. Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527), 2002

298 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/cad35a74b0552c899ad6c89904e16b07af87e110

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Shaocheng Tong
2. Shuzhi Sam Ge
3. Wei He
4. Changyin Sun
5. Tie-shan Li
6. Zhao Yin
7. Dan Wang
8. Tong-heng Lee

In Computer Networks and Communications

1. Shaocheng Tong
2. Shuzhi Sam Ge
3. Tie-shan Li
4. Wei He
5. Dan Wang
6. Changyin Sun
7. Gang Feng
8. Tong-heng Lee

In Control and Systems Engineering

1. Shaocheng Tong
2. Shuzhi Sam Ge
3. Wei He
4. Tie-shan Li
5. Yong-ming Li
6. Dan Wang
7. Changyin Sun
8. Gang Feng

Citations in the corpus

• A DSC Approach to Robust Adaptive NN Tracking Control for Strict-Feedback Nonlinear Systems (2008, methodology): “Note that this phenomenon is very common in the universal approximator-based adaptive control schemes, such as in [13], [15], [17], [21]–[24], [27], and so on.”
• Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients (2003, methodology): “[18] , “Adaptive stochastic approximation by the simultaneous perturbation method,”IEEE Trans.”
• Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients (2004, methodology): “Noting Assumption 2, we have the following inequality [18]:”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, background): “Then the following property is valid: where Lemma 3 [67]: The basis function of the Gaussian radial basis function neural network with ˆ X = X − γ ψ being the input vector, where ψ is a bounded vector and γ is positive is given as where S t is a bounded vector function.”