Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control: Computationally-light non-lifted data-driven norm-optimal iterative learning control

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control: Computationally-light non-lifted data-driven norm-optimal iterative learning control
Συγγραφείς: Ronghu Chi, Zhongsheng Hou, Shangtai Jin, Biao Huang
Πηγή: Asian Journal of Control. 20:115-124
Στοιχεία εκδότη: Wiley, 2017.
Έτος έκδοσης: 2017
Θεματικοί όροι: 0209 industrial biotechnology, Discrete-time control/observation systems, computationally-light algorithm, data-driven control approach, Learning and adaptive systems in artificial intelligence, norm optimal ILC, Nonlinear systems in control theory, 02 engineering and technology, Computational methods in systems theory, nonlinear discrete-time systems
Περιγραφή: Computational complexity and model dependence are two significant limitations on lifted norm optimal iterative learning control (NOILC). To overcome these two issues and retain monotonic convergence in iteration, this paper proposes a computationally‐efficient non‐lifted NOILC strategy for nonlinear discrete‐time systems via a data‐driven approach. First, an iteration‐dependent linear representation of the controlled nonlinear process is introduced by using a dynamical linearization method in the iteration direction. The non‐lifted NOILC is then proposed by utilizing the input and output measurements only, instead of relying on an explicit model of the plant. The computational complexity is reduced by avoiding matrix operation in the learning law. This greatly facilitates its practical application potential. The proposed control law executes in real‐time and utilizes more control information at previous time instants within the same iteration, which can help improve the control performance. The effectiveness of the non‐lifted data‐driven NOILC is demonstrated by rigorous analysis along with a simulation on a batch chemical reaction process.
Τύπος εγγράφου: Article
Περιγραφή αρχείου: application/xml
Γλώσσα: English
ISSN: 1934-6093
1561-8625
DOI: 10.1002/asjc.1569
Σύνδεσμος πρόσβασης: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asjc.1569
https://onlinelibrary.wiley.com/doi/10.1002/asjc.1569
Rights: Wiley Online Library User Agreement
Αριθμός Καταχώρησης: edsair.doi.dedup.....28ec52a19e2e4fa7d95f1a06b38730d9
Βάση Δεδομένων: OpenAIRE
Περιγραφή
ISSN:19346093
15618625
DOI:10.1002/asjc.1569