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Full Description
Advances in Process Control with Real Applications presents various advanced controllers, including the formulation, design, and implementation of various advanced control strategies for a wide variety of processes. These strategies include generalized predictive control with and without constraints; linear and nonlinear model predictive control; dynamic matrix control; nonlinear control, such as generic model control, globally linearizing control, and nonlinear internal model control; optimal and optimizing control; inferential control; intelligent control based on fuzzy reasoning and neural networks; and controllers based on stochastic and evolutionary optimization.
This book will be highly beneficial to students, researchers, and industry professionals working in process design, process monitoring, process systems engineering, process operations and control, and related areas.
Contents
1. Advanced process control & its significance
2. Types of models for advanced controllers
3. Role of state estimation in advanced process control
4. Significance of stochastic and evolutionary methods in advanced process control
5. Advanced process control algorithms
6. Applications of generalized predictive control
7. Applications of linear model predictive control to nonlinear systems
8. Applications of nonlinear model predictive control
9. Applications of generic model control
10. Applications of globally linearizing control
11. Applications of nonlinear internal model control
12. Applications of optimal control
13. Applications of optimizing control
14. Applications of inferential control
15. Applications of fuzzy logic control
16. Applications of neural network control
17. Applications of radial basis function network control
18. Nonlinear process control based on evolutionary and stochastic optimizers
19. Future trends and challenges
