Identification and control of hammerstein-type nonlinear process

Journal of the Chinese Institute of Chemical Engineers

Volumn 32, Issue 4, 2001, Pages 361-371

  Lee, M W ^a   Huang, H P ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Controller design; Hammerstein; Identification; Relay feedback

Indexed keywords

CONTROL NONLINEARITIES; LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PROCESS CONTROL; TRANSFER FUNCTIONS;

HAMMERSTEIN-TYPE NONLINEAR PROCESS;

NONLINEAR CONTROL SYSTEMS;

FEEDBACK; NON-LINEAR SYSTEM; PROCESS CONTROL;

EID: 0035387447     PISSN: 03681653     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. A new method for the identification of hammerstein model
2. Structure detection and model validity tests in the identification of nonlinear systems
3. Nonlinear adaptive control via laguerre expansion of volterra kemels
4. Structure identification of quadratic block-oriented models based on estimated volterra kemels
5. Structure identification of nonlinear dynamic systems - A survey on input/output approaches
6. Input-output linearization of general nonlinear processes
7. Autotuning for model-based PID controllers
8. Identification of wiener model using relay feedback test
9. A new approach to the identification of pH process based on the wiener model
10. A nonparametric polynomian identification algorithm for the Hammerstein system
11. Structure identification for nonlinear models
12. Identification of Hammerstein models using genetic algorithms
13. Nonlinear auto-tune identification
14. Nonlinear structure identification of chemical engineering
15. Nonlinear internal model control strategy for neutral network models
16. Use of approximate models and exact linearization for control of nonlinear processes
17. Gray-box identification of block-oriented nonlinear models
18. Iterative algorithm for parameter identification of Hammerstein system with two-segment nonlinearities
19. Recursive prediction error identification using the nonlinear Wiener model