Data-Driven Nonlinear Control Design Using Virtual-Reference Feedback Tuning Based on the Block-Oriented Modeling of Nonlinear Systems
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Summary
A novel data-driven method for nonlinear control design based on the virtual-reference feedback tuning (VRFT) framework and block-oriented modeling of nonlinear systems is presented.
- Type
- article
- Published
- 2018-05-07
- Cited by
- 18
- References
- 0
- OpenAlex
- https://openalex.org/W2799472139
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:103385477
Keywords
Nonlinear system, Control theory (sociology), Computer science, Benchmark (surveying), Block (permutation group theory)
References
Cited by
- Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance
- Data-Driven Adaptive Quality Control Under Uncertain Conditions for a Cyber-Pharmaceutical-Development System
- Data-driven Two Degrees of Freedom Controller Design for MIMO System via VRFT Approach
- Combined Signals Based Identification Scheme for the Hammerstein-Wiener System with Process Noise
- A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise
- Data-driven order reduction in Hammerstein-Wiener models of plasma dynamics
- A new approach of H∞ filtering for combustion systems using optical instrumentation.
- Data-driven Learning Algorithm of Neural Fuzzy Based Hammerstein-Wiener System
- Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models
- Nonlinear Identification and Control of the Hammerstein System with Application to pH Neutralization Process
- Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing
- Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise
- Separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis
- Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature
- Parameters estimation for the Hammerstein‐Wiener models with colored noise based on hybrid signals
- Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine
- Parameter Identification for the Hammerstein-Wiener Nonlinear Time Delay Systems with Process Noises
- Robust series cascade controller design for non-minimum phase system via novel data-driven VRFT approach
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