Abstract
Robust operation of complex nonlinear and time varying processes create several challenges for the multivariable control system design engineer. Optimal controller designs are typically model based and a design that delivered excellent performance under nominal conditions may produce poor results for nonlinear systems when operating conditions are varied. Changes in operating conditions may occur through a setpoint change or nonstation-ary disturbance. Setpoint changes to produce different grades of products and nonstationary disturbances such as variations in input properties are common in many process industries. When such changes occur, the control system may have poor performance since the original process representation can become inaccurate. In addition, any sensor or actuator faults can degrade multivariable control system performance.
Keywords
- Controller Design
- Inference Engine
- Control System Design
- Process Control System
- Distribute Control System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Kendra, S.J., Basila, M.R., Çinar, A. (1997). Intelligent Process Control with Supervisory Knowledge-Based Systems. In: Tzafestas, S.G. (eds) Methods and Applications of Intelligent Control. Microprocessor-Based and Intelligent Systems Engineering, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5498-7_5
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DOI: https://doi.org/10.1007/978-94-011-5498-7_5
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