Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control

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...

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Vydáno v:Asian journal of control Ročník 20; číslo 1; s. 115 - 124
Hlavní autoři: Chi, Ronghu, Hou, Zhongsheng, Jin, Shangtai, Huang, Biao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.01.2018
Témata:
Chemical reactions
Complexity
Computationally‐light algorithm
Computer simulation
Data‐driven control approach
Discrete time systems
Iterative methods
Learning
Nonlinear control
Nonlinear discrete‐time systems
Nonlinear systems
Norm optimal ILC
ISSN:1561-8625, 1934-6093
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Shrnutí: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.
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.1569