Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems

By Yanjun Liu, Chun Lung Philip Chen, Guoxing Wen, Shaocheng Tong. IEEE Transactions on Neural Networks, 2011

362 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/cd75ef2d024257a8fbc26cb3e92934a1813e5dd2

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. Changyin Sun
3. Chenguang Yang
4. Yiting Dong
5. Zhouhua Peng
6. Jun Wang
7. Yanjun Liu
8. Zhao Yin

In Computer Networks and Communications

1. Wei He
2. Changyin Sun
3. Yanjun Liu
4. Zhao Yin
5. Jun Wang
6. Chenguang Yang
7. Gang Tao
8. Bin Jiang

In Control and Systems Engineering

1. Wei He
2. Changyin Sun
3. Chenguang Yang
4. Yiting Dong
5. Jun Wang
6. Yanjun Liu
7. Zhouhua Peng
8. Mou Chen

Citations in the corpus

• Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation (2016, background): “In [34], an adaptive neural output feedback tracking control of nonlinear multiple-input multiple-output systems is studied.”
• Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation (2015, methodology): “To efficiently handle the system uncertainty in each subsystem, radial basis function NN (RBFNN) with the universal approximation capability is employed in [30] and [31].”
• Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2016, background): “An adaptive neural output feedback tracking control of uncertain nonlinear multiinput and multioutput (MIMO) systems in the discrete-time form is studied in [22].”
• Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “Most of the currently employed ANNs for artificial intelligence are based on statistical estimations [113], classification optimization [131] and control theory [105].”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, background): “The adaptive neural output feedback control is provided for a class of uncertain nonlinear SISO systems in [60]–[62].”
• Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks (2018, background): “From a practical perspective, output-feedback path-following control is of great importance [28]–[32].”
• Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks (2014, background): “Because it has been proven that neural networks (NNs) have the excellent approximation ability, the use of NNs in the proof of stability in nonlinear dynamic systems has become very popular and effective [20]–[24].”
• Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning (2017, background): “Thus, the continues-time controller needs to be transformed into a discrete-time version [46].”
• Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint (2016, methodology): “In [20] and [21], a higher order neural network is used to design an adaptive controller for a class of uncertain multiple-input multiple-output (MIMO) nonlinear systems.”
• Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015, background): “Neural networks can provide models for a large class of natural and artificial phenomena that are difficult to handle using classical parametric techniques [1] [2] [3] [4] [5].”