Fusion of inverse optimal and model predictive control strategies

Lütfi Ulusoy, Müjde Güzelkaya*, İbrahim Eksin

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4 Atıf (Scopus)

Özet

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1122-1134
Sayfa sayısı13
DergiTransactions of the Institute of Measurement and Control
Hacim42
Basın numarası6
DOI'lar
Yayın durumuYayınlandı - 1 Nis 2020

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Publisher Copyright:
© The Author(s) 2019.

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