A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control
By Tong Wang, Huijun Gao, Jianbin Qiu. IEEE Transactions on Neural Networks and Learning Systems, 2016
625 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/fe1c6df309603adb5edf266cf282615a725fa4d7
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In Control and Systems Engineering
Citations in the corpus
- Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems (2016, background): “Nevertheless, it is known that a large number of nonlinear industrial processes are inherently distributed in space, and their dynamics depend both on spatial position and time such as the fluid flow, heat conduction, and chemical reactor processes [15]–[20].”
- 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.”
- Recent Advances on Fuzzy-Model-Based Nonlinear Networked Control Systems: A Survey (2016, background): “tion technology in large-scale industrial processes has become evident [8]–[13], which is also indicated by the visions of cyber-physical systems.”
- Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone (2019, methodology): “In [13], the multirate networked industrial process control method was proposed in double-layer architecture by using the adaptive NN control.”
- Observer-Based Fault Detection for Nonlinear Systems With Sensor Fault and Limited Communication Capacity (2016, background): “Since a fault can undermine the performance and even the stability of a system, many results were reported in the field of fault detection for different types of systems during the past several decades, for example, [1]–[4].”
- Output Reachable Set Estimation and Verification for Multilayer Neural Networks (2017, background): “Applications include adaptive control [1, 2, 3, 4], pattern recognition [5, 6], game playing [7], autonomous vehicles [8] ,and many others.”
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit (2017, background): “, ARX, ARMA, NARX, and NARMAX [3] models), and neural network models [27, 14, 54, 1], to name only a few.”
- Network-Based Event-Triggered Control for Singular Systems With Quantizations (2016, neutral): “w(t) = { sgn (sin(t)) , if t ∈ [0, 10]”
- A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements (2016, neutral): “However, due to the complexity of large-scale networks, the measurement signals may be missing/fading during the network transmission resulting from various causes such as sensors aging, intermittent sensor failures, limited bandwidth, network congestion or accidental loss of some collected data [6,”