Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/246418 
Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 7 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 1-9
Verlag: 
Elsevier, Amsterdam
Zusammenfassung: 
The literature associated with system modeling and decision-making falls short in automating policy formation for correlated multiple-policy multiple-objective (MPMO) processes. Enthused by the effectiveness of multivariable control theory, this paper proposes the implementation of a multiple-input multiple-output (MIMO) controller as a guidance tool for designing policies. System dynamics is considered for modeling the dynamic behavior of MPMO systems. The model is then converted to a state-space system where objectives and policies are mapped to time-varying reference trajectories and inputs, respectively. Subsequently, the design of policies driving the system outputs to meet certain objective profiles is converted to a multivariable control problem. This paper also recommends a class of multivariable controllers that is suitable for this domain of applications. Numerical simulations are included to illustrate the effectiveness of the proposed systematic approach.
Schlagwörter: 
Control theory
Multivariable control
Policy design
Strategic planning
System dynamics
Iterative learning control
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