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Tuning of multivariable model predictive controllers through expert bandit feedback
- Source :
- International Journal of Control. 94:2650-2658
- Publication Year :
- 2020
- Publisher :
- Informa UK Limited, 2020.
-
Abstract
- For certain industrial control applications an explicit function capturing the nontrivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimization algorithm that can efficiently deal with a large number of variables, is used for optimizing the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.
- Subjects :
- 0209 industrial biotechnology
Implicit function
Computer science
Control (management)
Systems and Control (eess.SY)
02 engineering and technology
Electrical Engineering and Systems Science - Systems and Control
Computer Science Applications
020901 industrial engineering & automation
Optimization and Control (math.OC)
Control and Systems Engineering
Control theory
FOS: Electrical engineering, electronic engineering, information engineering
FOS: Mathematics
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Multivariable model
93C83, 93C85, 93C95, 90C56, 90C90
Mathematics - Optimization and Control
Closed loop
Subjects
Details
- ISSN :
- 13665820 and 00207179
- Volume :
- 94
- Database :
- OpenAIRE
- Journal :
- International Journal of Control
- Accession number :
- edsair.doi.dedup.....fbbbeefed0c3030270877d1f57956b77