Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes

[Display omitted] •GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturb...

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Bibliographic Details
Published in:Chemical engineering research & design 2017-01, Vol.117, p.265-273
Main Authors: Araújo, Rejane de B., Coelho, Antonio A.R.
Format: Article
Language:English
Subjects:
Behavior
Boilers
Chemical processes
Computer simulation
Controllers
Design optimization
Disturbance rejection
Generalized predictive controller
Genetic algorithm
Genetic algorithms
Mathematical models
Multiple objective analysis
Offset-free
Optimization
Polynomials
Predictive control
Reference tracking
Rejection
Robustness (mathematics)
Stability analysis
Studies
Weighting
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Summary:[Display omitted] •GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturbance rejection.•Case study in chemical processes. The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.
ISSN:0263-8762
1744-3563
DOI:10.1016/j.cherd.2016.10.038