Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer

By Mou Chen, Shuzhi Sam Ge. IEEE Transactions on Cybernetics, 2013

372 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/30b70d53b331e8b6e03edb36c304b1743bca2310

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. Wei He
2. Chenguang Yang
3. Changyin Sun
4. Zhao Yin
5. B. Xu
6. Peng Shi
7. Yongping Pan
8. Wenjie Dong

In Computer Networks and Communications

1. Wei He
2. Changyin Sun
3. Chenguang Yang
4. B. Xu
5. Gang Tao
6. Peng Shi
7. Bin Jiang
8. Yongping Pan

In Control and Systems Engineering

1. Wei He
2. Chenguang Yang
3. Mou Chen
4. Changyin Sun
5. Peng Shi
6. Gang Tao
7. Zhao Yin
8. Bin Jiang

Citations in the corpus

• Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle (2015, methodology): “In the literature of intelligent control, the design with the fuzzy logic system (FLS)/NN has been widely studied for both continuous systems [23]–[28] and discrete systems [29]–[33] to achieve semiglobally uniformly ultimately bounded (SGUUB) of the closed-loop system.”
• Disturbance Observer-Based Robust Control and Its Applications: 35th Anniversary Overview (2019, background): “linear systems, such as a Duffing–Holmes chaotic system and nonlinear multi-agent systems [83], [84].”
• Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System (2016, background): “Thus, a disturbance observer combing with a neural network or a fuzzy logic system can offer such advantages as rejecting disturbances and enhancing closed-loop robustness [63], [64] for 3-DOF model helicopters.”
• Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, background): “Accordingly, if we choose appropriate parameters for the proposed disturbance observer, the estimated error will converge to a compact set [46], [47].”
• Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems (2015, methodology): “Adaptive NN or fuzzy control combined backstepping technique provides a systematic methodology to solve the problem of linear in the unknown system parameters contained in the backstepping design [23]–[27].”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “where i = T i > 0 (i = 1, 2, 3) is the constant gain matrix, and σi > 0, i = 1, 2, 3 are small constants [81].”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, background): “In general, RBFNN can smoothly approximate any continuous function f (Z) over the compact set Z ∈ Rq to any arbitrary accuracy as [55]”