• Aniruddha Datta
Part of the Advances in Industrial Control book series (AIC)


The control of chemical processes is an important applications area for the field of automatic control. The design of advanced control systems for chemical process control is quite a challenging task since it requires the satisfaction of several physical constraints on the values of the controlled variables. Conventional control theory is not capable of incorporating such constraints directly into the control system design. Instead, constraints are handled in a mostly adhoc fashion, once the design has been carried out. Another important characteristic of conventional automatic control theory is that a considerable amount of effort is expended in first stabilizing an open loop unstable plant. However, for most process control applications, the plant is already open loop stable to start with thereby making the initial stabilization step unnecessary. What is therefore desirable for process control applications is a control scheme which directly handles constraints and also does not destabilize a plant which is stable to start with. Model predictive control schemes [13] apparently possess both these characteristics and this is what accounts for their immense popularity in process control applications.


Model Predictive Control Internal Model Control Model Predictive Control Scheme Dynamic Matrix Control Multiplicative Perturbation 
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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Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Aniruddha Datta
    • 1
  1. 1.Department of Electrical EngineeringTexas A & M UniversityCollege StationUSA

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