Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form

By B. Xu, Zhong-ke Shi, Chenguang Yang, Fuchun Sun. IEEE Transactions on Cybernetics, 2014

364 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3d119ecf55947bf0e0064196e976f0f5dc75b151

How researchers see this paper

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In Control and Systems Engineering

1. Shaocheng Tong
2. Wei He
3. Yong-ming Li
4. Yanjun Liu
5. Mou Chen
6. Shuzhi Sam Ge
7. Chun Lung Philip Chen
8. Changyin Sun

Citations in the corpus

• Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation (2015, background): “[33] proposed a novel composite neural DSC of a class of uncertain nonlinear strict-feedback systems without satisfying the matching conditions.”
• Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer (2015, background): “To deal with system uncertainties, neural networks (NNs) and fuzzy logic systems can be employed to compensate for the system uncertainty of the nonlinear system [42]–[48].”
• Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, methodology): “In [49], a novel composite learning law is first proposed using the estimation error for improving the accuracy of neural network.”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “In [58], a composite adaptive tracking control is studied for a class of uncertain nonlinear systems in strict-feedback form.”
• Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone (2016, methodology): “In this paper, the radial basis function neural network (RBFNN) is employed to approximate the unknown nonlinear continuous interconnection functions [31]–[33].”
• Hybrid Fuzzy Adaptive Output Feedback Control Design for Uncertain MIMO Nonlinear Systems With Time-Varying Delays and Input Saturation (2016, background): “Thus, according to (69) and [20], [38], [54], [57]–[59], it is concluded that all the signals of the closed-loop system are SGUUB.”
• Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay Systems (2017, background): “great number of NN or FLS-based nonlinear control methods have been published [19]–[26], in which [19]–[22] are based on NN and [23]–[26] are based on FLS.”
• Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System (2016, background): “Thus, various robust control schemes have been developed for uncertain nonlinear systems using fuzzy logic systems and neural networks [54]–[60].”