Research Output
Constraint neuro-model based predictive control
  Constraints are always present in the real life process. Control of processes without considering the constraints can lead to sub-optimal behaviour and may result in instability and violation of safety. Lack of a solid and applicable closed loop control theory in dealing with constraints cause that in the usual process-controlpractice either to ignore the constraints or the constraints are dealt with ad-hoc fix ups using split range controllers, overrides, min-max selectors with some logic and etc.

  • Date:

    01 January 2004

  • Publication Status:

    Published

  • DOI:

    10.1007/978-3-540-45240-9_38

  • Library of Congress:

    TA Engineering (General). Civil engineering (General)

  • Dewey Decimal Classification:

    620 Engineering and allied operations

  • Funders:

    Manchester Metropolitan University

Citation

Soufian, M., Rahman, A. A., Soufian, M., & Mokbe, A. A. (2004). Constraint neuro-model based predictive control. In A. Lotfi, & J. M. Garibaldi (Eds.), Applications and Science in Soft Computing (279-286). https://doi.org/10.1007/978-3-540-45240-9_38

Authors

Keywords

Artificial Neural Network Model; Model Predictive Control; Sequential Quadratic Programming; Integrate Square Error; Generalise Predictive Control

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